{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c4562f03-2077-4700-ac31-0a20d28e97a6",
   "metadata": {},
   "source": [
    "# Case Study - 🚑 911 Calls\n",
    "\n",
    "**Fire, Traffic and EMS(Emergency Medical Services) for Montgomery County, Pennsylvania (PA).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3a97e0b7-f8f1-41ea-93f1-ed4250eff9a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83755347-771e-4fe3-9807-a2f8454ae363",
   "metadata": {},
   "source": [
    "### Assuming we are data scientist working with 911. What kind of data would you require?\n",
    "- Call Logs\n",
    "- Call Volumes - Number of calls per day\n",
    "- Duration of calls\n",
    "- How many employees and their call engagements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "99c81ff6-7635-451a-a4bf-1dd395534894",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:10:52</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:29:21</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 14:39:21</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 16:47:36</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 16:56:52</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title            timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY  2015-12-10 17:10:52        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY  2015-12-10 17:29:21  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK  2015-12-10 14:39:21         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY  2015-12-10 16:47:36         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS  2015-12-10 16:56:52   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e  \n",
       "0      REINDEER CT & DEAD END  1  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1  \n",
       "2                    HAWS AVE  1  \n",
       "3          AIRY ST & SWEDE ST  1  \n",
       "4    CHERRYWOOD CT & DEAD END  1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = \"data/call_center/911.csv\"\n",
    "\n",
    "df = pd.read_csv(data_path)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "454ecd48-fc94-479b-97dc-053f9b23da23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(663522, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c9d0eb3b-b39a-4bf3-947e-6564a3422f2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['lat', 'lng', 'desc', 'zip', 'title', 'timeStamp', 'twp', 'addr', 'e'], dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "65368d30-409e-4def-b774-30f68c42dc70",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 663522 entries, 0 to 663521\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count   Dtype  \n",
      "---  ------     --------------   -----  \n",
      " 0   lat        663522 non-null  float64\n",
      " 1   lng        663522 non-null  float64\n",
      " 2   desc       663522 non-null  object \n",
      " 3   zip        583323 non-null  float64\n",
      " 4   title      663522 non-null  object \n",
      " 5   timeStamp  663522 non-null  object \n",
      " 6   twp        663229 non-null  object \n",
      " 7   addr       663522 non-null  object \n",
      " 8   e          663522 non-null  int64  \n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 45.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "311410a9-829e-4c35-8e76-400294b1b716",
   "metadata": {},
   "source": [
    "## Data Dictionary\n",
    "\n",
    "- **lat** : String variable, Latitude\n",
    "- **lng**: String variable, Longitude\n",
    "- **desc**: String variable, Description of the Emergency Call\n",
    "- **zip**: String variable, Zipcode\n",
    "- **title**: String variable, Title\n",
    "- **timeStamp**: String variable, `YYYY-MM-DD HH:MM:SS`\n",
    "- **twp**: String variable, Township\n",
    "- **addr**: String variable, Address\n",
    "- **e**: String variable, Dummy variable (always 1)\n",
    "\n",
    "All good data analysis projects begin with trying to answer questions. Now that we know what column category data we have let's think of some questions or insights we would like to obtain from the data. So here's a list of questions we'll try to answer using our data analysis skills!\n",
    "\n",
    "**First some basic questions: (ToDo)**\n",
    "- From where the calls come most?\n",
    "- Which are top townships for calls?\n",
    "- How many unique title?\n",
    "- What is the reason for most calls?\n",
    "\n",
    "**Problem Statement: Estimating Emergency Calls at a 911 Call Center**\n",
    "\n",
    "**Background:**\n",
    "You're managing a 911 call center and you want to estimate how many emergency calls you can expect to receive during different hours of the day.\n",
    "\n",
    "Can we estimate the number of emergency calls that the 911 call center may receive in a given time period?\n",
    "\n",
    "If yes, this information can help in resource allocation, staffing, and capacity planning."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fee420c-1c00-4b12-a332-cc8a91b38d6b",
   "metadata": {},
   "source": [
    "### How many calls per day per hour?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4b1dea87-f4ae-47d2-9fa7-a37036839a06",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 663522 entries, 0 to 663521\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count   Dtype         \n",
      "---  ------     --------------   -----         \n",
      " 0   lat        663522 non-null  float64       \n",
      " 1   lng        663522 non-null  float64       \n",
      " 2   desc       663522 non-null  object        \n",
      " 3   zip        583323 non-null  float64       \n",
      " 4   title      663522 non-null  object        \n",
      " 5   timeStamp  663522 non-null  datetime64[ns]\n",
      " 6   twp        663229 non-null  object        \n",
      " 7   addr       663522 non-null  object        \n",
      " 8   e          663522 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(3), int64(1), object(4)\n",
      "memory usage: 45.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df['timeStamp'] = pd.to_datetime(df['timeStamp'])\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "60c9dc33-311b-4e7a-8d6a-4eda51254f6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         17\n",
       "1         17\n",
       "2         14\n",
       "3         16\n",
       "4         16\n",
       "          ..\n",
       "663517    15\n",
       "663518    15\n",
       "663519    15\n",
       "663520    15\n",
       "663521    15\n",
       "Name: timeStamp, Length: 663522, dtype: int32"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['timeStamp'].dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4e24a3ab-a854-4430-82ed-6b47a7416c76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         2015-12-10\n",
       "1         2015-12-10\n",
       "2         2015-12-10\n",
       "3         2015-12-10\n",
       "4         2015-12-10\n",
       "             ...    \n",
       "663517    2020-07-29\n",
       "663518    2020-07-29\n",
       "663519    2020-07-29\n",
       "663520    2020-07-29\n",
       "663521    2020-07-29\n",
       "Name: timeStamp, Length: 663522, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['timeStamp'].dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1cb51426-f598-45e5-abaf-c257cc22df80",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
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       "      <th>hour</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:10:52</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:29:21</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 14:39:21</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 16:47:36</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 16:56:52</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title           timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY 2015-12-10 17:10:52        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:29:21  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 14:39:21         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY 2015-12-10 16:47:36         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS 2015-12-10 16:56:52   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e  hour        date  \n",
       "0      REINDEER CT & DEAD END  1    17  2015-12-10  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    17  2015-12-10  \n",
       "2                    HAWS AVE  1    14  2015-12-10  \n",
       "3          AIRY ST & SWEDE ST  1    16  2015-12-10  \n",
       "4    CHERRYWOOD CT & DEAD END  1    16  2015-12-10  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['hour'] = df['timeStamp'].dt.hour\n",
    "\n",
    "df['date'] = df['timeStamp'].dt.strftime('%Y-%m-%d')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e999e1cd-d924-474d-8b90-ec29ab13eaf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['lat', 'lng', 'desc', 'zip', 'title', 'timeStamp', 'twp', 'addr', 'e',\n",
       "       'hour', 'date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ffcf14a8-18a4-4530-b354-68b45ebd71fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>hour</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:10:52</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:29:21</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>40.102398</td>\n",
       "      <td>-75.291458</td>\n",
       "      <td>BLUEROUTE  &amp; RAMP I476 NB TO CHEMICAL RD; PLYM...</td>\n",
       "      <td>19462.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2015-12-10 17:35:41</td>\n",
       "      <td>PLYMOUTH</td>\n",
       "      <td>BLUEROUTE  &amp; RAMP I476 NB TO CHEMICAL RD</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>40.231990</td>\n",
       "      <td>-75.251891</td>\n",
       "      <td>RT202 PKWY &amp; KNAPP RD; MONTGOMERY; 2015-12-10 ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2015-12-10 17:33:50</td>\n",
       "      <td>MONTGOMERY</td>\n",
       "      <td>RT202 PKWY &amp; KNAPP RD</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>40.174131</td>\n",
       "      <td>-75.098491</td>\n",
       "      <td>BYBERRY AVE &amp; S WARMINSTER RD; UPPER MORELAND;...</td>\n",
       "      <td>19040.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2015-12-10 17:15:49</td>\n",
       "      <td>UPPER MORELAND</td>\n",
       "      <td>BYBERRY AVE &amp; S WARMINSTER RD</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>40.062974</td>\n",
       "      <td>-75.135914</td>\n",
       "      <td>OLD YORK RD &amp; VALLEY RD; CHELTENHAM; 2015-12-1...</td>\n",
       "      <td>19027.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2015-12-10 17:12:47</td>\n",
       "      <td>CHELTENHAM</td>\n",
       "      <td>OLD YORK RD &amp; VALLEY RD</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40.097222</td>\n",
       "      <td>-75.376195</td>\n",
       "      <td>SCHUYLKILL EXPY &amp; CROTON RD UNDERPASS; UPPER M...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2015-12-10 17:09:49</td>\n",
       "      <td>UPPER MERION</td>\n",
       "      <td>SCHUYLKILL EXPY &amp; CROTON RD UNDERPASS</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>40.223778</td>\n",
       "      <td>-75.235399</td>\n",
       "      <td>STUMP RD &amp; WITCHWOOD DR; MONTGOMERY; 2015-12-1...</td>\n",
       "      <td>18936.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2015-12-10 17:31:00</td>\n",
       "      <td>MONTGOMERY</td>\n",
       "      <td>STUMP RD &amp; WITCHWOOD DR</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>40.243258</td>\n",
       "      <td>-75.286552</td>\n",
       "      <td>SUSQUEHANNA AVE &amp; W MAIN ST;  LANSDALE; Statio...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: RESPIRATORY EMERGENCY</td>\n",
       "      <td>2015-12-10 17:42:44</td>\n",
       "      <td>LANSDALE</td>\n",
       "      <td>SUSQUEHANNA AVE &amp; W MAIN ST</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>40.312181</td>\n",
       "      <td>-75.574260</td>\n",
       "      <td>CHARLOTTE ST &amp; MILES RD;  NEW HANOVER; Station...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 17:41:54</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>CHARLOTTE ST &amp; MILES RD</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>40.114239</td>\n",
       "      <td>-75.338508</td>\n",
       "      <td>PENN ST &amp; ARCH ST;  NORRISTOWN; Station 308A; ...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: VEHICLE ACCIDENT</td>\n",
       "      <td>2015-12-10 17:43:29</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>PENN ST &amp; ARCH ST</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>40.209337</td>\n",
       "      <td>-75.135266</td>\n",
       "      <td>COUNTY LINE RD &amp; WILLOW DR; HORSHAM; 2015-12-1...</td>\n",
       "      <td>18974.0</td>\n",
       "      <td>Traffic: DISABLED VEHICLE -</td>\n",
       "      <td>2015-12-10 17:45:23</td>\n",
       "      <td>HORSHAM</td>\n",
       "      <td>COUNTY LINE RD &amp; WILLOW DR</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40.114239</td>\n",
       "      <td>-75.338508</td>\n",
       "      <td>PENN ST &amp; ARCH ST; NORRISTOWN; 2015-12-10 @ 17...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2015-12-10 17:43:45</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>PENN ST &amp; ARCH ST</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>40.117948</td>\n",
       "      <td>-75.209848</td>\n",
       "      <td>CHURCH RD &amp; REDCOAT DR; WHITEMARSH; 2015-12-10...</td>\n",
       "      <td>19031.0</td>\n",
       "      <td>Traffic: DISABLED VEHICLE -</td>\n",
       "      <td>2015-12-10 17:53:22</td>\n",
       "      <td>WHITEMARSH</td>\n",
       "      <td>CHURCH RD &amp; REDCOAT DR</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>40.199006</td>\n",
       "      <td>-75.300058</td>\n",
       "      <td>LILAC CT &amp; PRIMROSE DR; UPPER GWYNEDD; 2015-12...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>Fire: APPLIANCE FIRE</td>\n",
       "      <td>2015-12-10 17:59:24</td>\n",
       "      <td>UPPER GWYNEDD</td>\n",
       "      <td>LILAC CT &amp; PRIMROSE DR</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>40.153268</td>\n",
       "      <td>-75.189558</td>\n",
       "      <td>SUMMIT AVE &amp; RT309 UNDERPASS; UPPER DUBLIN; 20...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Traffic: VEHICLE ACCIDENT -</td>\n",
       "      <td>2015-12-10 17:58:22</td>\n",
       "      <td>UPPER DUBLIN</td>\n",
       "      <td>SUMMIT AVE &amp; RT309 UNDERPASS</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          lat        lng                                               desc  \\\n",
       "0   40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1   40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "9   40.102398 -75.291458  BLUEROUTE  & RAMP I476 NB TO CHEMICAL RD; PLYM...   \n",
       "10  40.231990 -75.251891  RT202 PKWY & KNAPP RD; MONTGOMERY; 2015-12-10 ...   \n",
       "12  40.174131 -75.098491  BYBERRY AVE & S WARMINSTER RD; UPPER MORELAND;...   \n",
       "13  40.062974 -75.135914  OLD YORK RD & VALLEY RD; CHELTENHAM; 2015-12-1...   \n",
       "14  40.097222 -75.376195  SCHUYLKILL EXPY & CROTON RD UNDERPASS; UPPER M...   \n",
       "15  40.223778 -75.235399  STUMP RD & WITCHWOOD DR; MONTGOMERY; 2015-12-1...   \n",
       "16  40.243258 -75.286552  SUSQUEHANNA AVE & W MAIN ST;  LANSDALE; Statio...   \n",
       "17  40.312181 -75.574260  CHARLOTTE ST & MILES RD;  NEW HANOVER; Station...   \n",
       "18  40.114239 -75.338508  PENN ST & ARCH ST;  NORRISTOWN; Station 308A; ...   \n",
       "19  40.209337 -75.135266  COUNTY LINE RD & WILLOW DR; HORSHAM; 2015-12-1...   \n",
       "20  40.114239 -75.338508  PENN ST & ARCH ST; NORRISTOWN; 2015-12-10 @ 17...   \n",
       "21  40.117948 -75.209848  CHURCH RD & REDCOAT DR; WHITEMARSH; 2015-12-10...   \n",
       "22  40.199006 -75.300058  LILAC CT & PRIMROSE DR; UPPER GWYNEDD; 2015-12...   \n",
       "24  40.153268 -75.189558  SUMMIT AVE & RT309 UNDERPASS; UPPER DUBLIN; 20...   \n",
       "\n",
       "        zip                        title           timeStamp  \\\n",
       "0   19525.0       EMS: BACK PAINS/INJURY 2015-12-10 17:10:52   \n",
       "1   19446.0      EMS: DIABETIC EMERGENCY 2015-12-10 17:29:21   \n",
       "9   19462.0  Traffic: VEHICLE ACCIDENT - 2015-12-10 17:35:41   \n",
       "10      NaN  Traffic: VEHICLE ACCIDENT - 2015-12-10 17:33:50   \n",
       "12  19040.0  Traffic: VEHICLE ACCIDENT - 2015-12-10 17:15:49   \n",
       "13  19027.0  Traffic: VEHICLE ACCIDENT - 2015-12-10 17:12:47   \n",
       "14      NaN  Traffic: VEHICLE ACCIDENT - 2015-12-10 17:09:49   \n",
       "15  18936.0  Traffic: VEHICLE ACCIDENT - 2015-12-10 17:31:00   \n",
       "16  19446.0   EMS: RESPIRATORY EMERGENCY 2015-12-10 17:42:44   \n",
       "17  19525.0               EMS: DIZZINESS 2015-12-10 17:41:54   \n",
       "18  19401.0        EMS: VEHICLE ACCIDENT 2015-12-10 17:43:29   \n",
       "19  18974.0  Traffic: DISABLED VEHICLE - 2015-12-10 17:45:23   \n",
       "20  19401.0  Traffic: VEHICLE ACCIDENT - 2015-12-10 17:43:45   \n",
       "21  19031.0  Traffic: DISABLED VEHICLE - 2015-12-10 17:53:22   \n",
       "22  19446.0         Fire: APPLIANCE FIRE 2015-12-10 17:59:24   \n",
       "24      NaN  Traffic: VEHICLE ACCIDENT - 2015-12-10 17:58:22   \n",
       "\n",
       "                  twp                                      addr  e  hour  \\\n",
       "0         NEW HANOVER                    REINDEER CT & DEAD END  1    17   \n",
       "1   HATFIELD TOWNSHIP                BRIAR PATH & WHITEMARSH LN  1    17   \n",
       "9            PLYMOUTH  BLUEROUTE  & RAMP I476 NB TO CHEMICAL RD  1    17   \n",
       "10         MONTGOMERY                     RT202 PKWY & KNAPP RD  1    17   \n",
       "12     UPPER MORELAND             BYBERRY AVE & S WARMINSTER RD  1    17   \n",
       "13         CHELTENHAM                   OLD YORK RD & VALLEY RD  1    17   \n",
       "14       UPPER MERION     SCHUYLKILL EXPY & CROTON RD UNDERPASS  1    17   \n",
       "15         MONTGOMERY                   STUMP RD & WITCHWOOD DR  1    17   \n",
       "16           LANSDALE               SUSQUEHANNA AVE & W MAIN ST  1    17   \n",
       "17        NEW HANOVER                   CHARLOTTE ST & MILES RD  1    17   \n",
       "18         NORRISTOWN                         PENN ST & ARCH ST  1    17   \n",
       "19            HORSHAM                COUNTY LINE RD & WILLOW DR  1    17   \n",
       "20         NORRISTOWN                         PENN ST & ARCH ST  1    17   \n",
       "21         WHITEMARSH                    CHURCH RD & REDCOAT DR  1    17   \n",
       "22      UPPER GWYNEDD                    LILAC CT & PRIMROSE DR  1    17   \n",
       "24       UPPER DUBLIN              SUMMIT AVE & RT309 UNDERPASS  1    17   \n",
       "\n",
       "          date  \n",
       "0   2015-12-10  \n",
       "1   2015-12-10  \n",
       "9   2015-12-10  \n",
       "10  2015-12-10  \n",
       "12  2015-12-10  \n",
       "13  2015-12-10  \n",
       "14  2015-12-10  \n",
       "15  2015-12-10  \n",
       "16  2015-12-10  \n",
       "17  2015-12-10  \n",
       "18  2015-12-10  \n",
       "19  2015-12-10  \n",
       "20  2015-12-10  \n",
       "21  2015-12-10  \n",
       "22  2015-12-10  \n",
       "24  2015-12-10  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's groupby based on the date and hour\n",
    "\n",
    "grouped_df = df.groupby(['date', 'hour'])\n",
    "\n",
    "grouped_df.get_group(name=('2015-12-10', 17))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a2e3735b-5d74-4f7b-a85e-9a944024f4fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date        hour\n",
       "2015-12-10  14       1\n",
       "            15       1\n",
       "            16       6\n",
       "            17      16\n",
       "            18      26\n",
       "                    ..\n",
       "2020-07-29  11      17\n",
       "            12      15\n",
       "            13      22\n",
       "            14      16\n",
       "            15      26\n",
       "Name: timeStamp, Length: 40546, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_df['timeStamp'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c4934be6-dfed-4124-a049-717e5450924f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "      <th>timeStamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-10</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-12-10</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-12-10</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-12-10</td>\n",
       "      <td>17</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-12-10</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40541</th>\n",
       "      <td>2020-07-29</td>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40542</th>\n",
       "      <td>2020-07-29</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40543</th>\n",
       "      <td>2020-07-29</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40544</th>\n",
       "      <td>2020-07-29</td>\n",
       "      <td>14</td>\n",
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       "    <tr>\n",
       "      <th>40545</th>\n",
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       "             date  hour  timeStamp\n",
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       "...           ...   ...        ...\n",
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       "40542  2020-07-29    12         15\n",
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       "40544  2020-07-29    14         16\n",
       "40545  2020-07-29    15         26\n",
       "\n",
       "[40546 rows x 3 columns]"
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     },
     "execution_count": 14,
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       "         date  hour  timeStamp\n",
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       "2  2015-12-10    16          6\n",
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       "         date  hour  calls\n",
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       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
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       "      <td>20.0</td>\n",
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       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>13.0</td>\n",
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       "      <th>2020-07-28</th>\n",
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       "    <tr>\n",
       "      <th>2020-07-29</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "hour          0     1    2     3    4     5     6     7     8     9   ...  \\\n",
       "date                                                                  ...   \n",
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       "2015-12-12   7.0   5.0  5.0  11.0  5.0  11.0   3.0  14.0   5.0  12.0  ...   \n",
       "2015-12-13   9.0   5.0  5.0   8.0  2.0   4.0   8.0  13.0  14.0  11.0  ...   \n",
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       "...          ...   ...  ...   ...  ...   ...   ...   ...   ...   ...  ...   \n",
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       "2020-07-26   6.0   7.0  8.0   8.0  6.0   3.0   7.0   6.0  13.0  16.0  ...   \n",
       "2020-07-27  11.0   7.0  4.0   3.0  4.0  10.0   8.0  14.0  10.0  14.0  ...   \n",
       "2020-07-28   6.0   4.0  3.0   8.0  7.0   8.0  13.0  17.0  32.0  20.0  ...   \n",
       "2020-07-29   6.0  11.0  4.0  12.0  7.0   7.0  10.0  13.0  15.0  22.0  ...   \n",
       "\n",
       "hour          14    15    16    17    18    19    20    21    22    23  \n",
       "date                                                                    \n",
       "2015-12-10   1.0   1.0   6.0  16.0  26.0  20.0  15.0  11.0  10.0   8.0  \n",
       "2015-12-11  23.0  28.0  27.0  39.0  24.0  32.0  11.0  12.0  16.0   7.0  \n",
       "2015-12-12  20.0  28.0  30.0  22.0  24.0  37.0   9.0  29.0  21.0  19.0  \n",
       "2015-12-13  18.0  17.0  14.0  22.0  28.0  16.0  14.0   8.0  17.0   6.0  \n",
       "2015-12-14  16.0  28.0  33.0  31.0  38.0  22.0  20.0  19.0  10.0  10.0  \n",
       "...          ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "2020-07-25  19.0  23.0  28.0  18.0  15.0  13.0  10.0  12.0  10.0   9.0  \n",
       "2020-07-26  26.0  13.0  12.0  19.0  18.0  14.0  20.0  15.0   8.0   5.0  \n",
       "2020-07-27  20.0  20.0  19.0  15.0  15.0  10.0  35.0  13.0   8.0   8.0  \n",
       "2020-07-28  22.0  17.0  31.0  16.0  13.0  16.0   8.0  13.0  13.0   9.0  \n",
       "2020-07-29  16.0  26.0   NaN   NaN   NaN   NaN   NaN   NaN   NaN   NaN  \n",
       "\n",
       "[1694 rows x 24 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(data=calls,\n",
    "               values='calls',\n",
    "               index='date',\n",
    "               columns='hour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "84fe4b22-dd35-433f-91cc-57dc8bf9016a",
   "metadata": {},
   "outputs": [],
   "source": [
    "report = pd.pivot_table(data=calls,\n",
    "               values='calls',\n",
    "               index='date',\n",
    "               columns='hour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f4c4a114-9691-49f7-b0e0-0045c140c621",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hour\n",
       "0      7\n",
       "1      8\n",
       "2     17\n",
       "3     14\n",
       "4     11\n",
       "5     11\n",
       "6      2\n",
       "7      4\n",
       "8      1\n",
       "9      2\n",
       "10     2\n",
       "11     3\n",
       "12     2\n",
       "13     4\n",
       "14     1\n",
       "15     1\n",
       "16     2\n",
       "17     2\n",
       "18     2\n",
       "19     3\n",
       "20     2\n",
       "21     2\n",
       "22     3\n",
       "23     4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5e4e0672-26e6-43c6-b740-07c49042d3c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>hour</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-11</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-12</th>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-13</th>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-14</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-25</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-26</th>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-27</th>\n",
       "      <td>11.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-28</th>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-07-29</th>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1694 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "hour          0     1    2     3    4     5     6     7     8     9   ...  \\\n",
       "date                                                                  ...   \n",
       "2015-12-10   0.0   0.0  0.0   0.0  0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "2015-12-11   3.0   4.0  4.0   9.0  6.0   5.0  10.0  11.0  29.0  11.0  ...   \n",
       "2015-12-12   7.0   5.0  5.0  11.0  5.0  11.0   3.0  14.0   5.0  12.0  ...   \n",
       "2015-12-13   9.0   5.0  5.0   8.0  2.0   4.0   8.0  13.0  14.0  11.0  ...   \n",
       "2015-12-14   4.0  11.0  4.0   3.0  4.0  10.0  15.0  27.0  25.0  21.0  ...   \n",
       "...          ...   ...  ...   ...  ...   ...   ...   ...   ...   ...  ...   \n",
       "2020-07-25  16.0   8.0  2.0   8.0  3.0   6.0   5.0   7.0  14.0  14.0  ...   \n",
       "2020-07-26   6.0   7.0  8.0   8.0  6.0   3.0   7.0   6.0  13.0  16.0  ...   \n",
       "2020-07-27  11.0   7.0  4.0   3.0  4.0  10.0   8.0  14.0  10.0  14.0  ...   \n",
       "2020-07-28   6.0   4.0  3.0   8.0  7.0   8.0  13.0  17.0  32.0  20.0  ...   \n",
       "2020-07-29   6.0  11.0  4.0  12.0  7.0   7.0  10.0  13.0  15.0  22.0  ...   \n",
       "\n",
       "hour          14    15    16    17    18    19    20    21    22    23  \n",
       "date                                                                    \n",
       "2015-12-10   1.0   1.0   6.0  16.0  26.0  20.0  15.0  11.0  10.0   8.0  \n",
       "2015-12-11  23.0  28.0  27.0  39.0  24.0  32.0  11.0  12.0  16.0   7.0  \n",
       "2015-12-12  20.0  28.0  30.0  22.0  24.0  37.0   9.0  29.0  21.0  19.0  \n",
       "2015-12-13  18.0  17.0  14.0  22.0  28.0  16.0  14.0   8.0  17.0   6.0  \n",
       "2015-12-14  16.0  28.0  33.0  31.0  38.0  22.0  20.0  19.0  10.0  10.0  \n",
       "...          ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "2020-07-25  19.0  23.0  28.0  18.0  15.0  13.0  10.0  12.0  10.0   9.0  \n",
       "2020-07-26  26.0  13.0  12.0  19.0  18.0  14.0  20.0  15.0   8.0   5.0  \n",
       "2020-07-27  20.0  20.0  19.0  15.0  15.0  10.0  35.0  13.0   8.0   8.0  \n",
       "2020-07-28  22.0  17.0  31.0  16.0  13.0  16.0   8.0  13.0  13.0   9.0  \n",
       "2020-07-29  16.0  26.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "\n",
       "[1694 rows x 24 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report.fillna(0, inplace=True)\n",
    "\n",
    "report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "29ed097f-65f5-4c70-b983-ad5b16cf9fa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='hour'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CLA4FKIQQQgixOBSgEEIIIcTi0DZjQojVsZY6G9YyTkIsEQUohBCrYqxSqb+/P2JiYiyqUqm1jJMQS0VLPIQQq5GZmQmVSoXg4GAkJiYiNTUViYmJCA4OhkqlQmZmZmsPEYD1jJMQS0al7gkhVkGj0WDatGkIDg7GkiVLYGf39+crrVYLpVKJgoICbNy4sVWXUaxlnIS0BiHXb5pBIYRYhZycHBQXF2Pq1Kl6F30AsLOzw5QpU1BUVIScnJxWGmEDaxknIZaOAhRCiFW4desWAKB79+5G72dvZ9u1FmsZJyGWjgIUQohV8PHxAQAUFBQYvZ+9nW3XWqxlnIRYOgpQCCFWITQ0FP7+/ti8eTO0Wq3efVqtFlu2bEFAQABCQ0NbaYQNrGWchFg6ClAIIVZBLpcjJiYGarUaSqUSeXl5qK6uRl5eHpRKJdRqNebNm9fqiafWMk5CLB3t4iGEWBVj9UUCAgIwb948i6ovYi3jJOR+EnL9pgCFEGJ1rKVCq7WMk5D7Rcj1myrJEkKsjlwuR1hYWGsPo0XWMk5CLBHloBBCCCHE4lCAQgghhBCLQwEKIYQQQiwOBSiEEEIIsTgUoBBCCCHE4lCAQgghhBCLQwEKIYQQQiwOBSiEEEIIsTgUoBBCCCHE4lCAQgghhBCLQwEKIYQQQiwOBSiEEEIIsTgUoBBCCCHE4lCAQgghhBCLQwEKIYQQQiyOoABl+fLlGDhwINzc3ODr64vx48cjPz9fr829e/cQGxsLHx8fuLq6YsKECSgpKdFrc+XKFURFRaF9+/bw9fXFW2+9hfr6evMfDSGEEELaBEEBSkZGBmJjY3HkyBGkpaWhrq4Ow4cPR1VVFddmwYIF2LVrF1JSUpCRkYHCwkI899xz3P0ajQZRUVGora3F4cOHsX79enz77bd47733xHtUhBBCCLFqMoZhGFO/+a+//oKvry8yMjIQERGB8vJydOzYEVu2bMHEiRMBAOfOncODDz4ItVqNIUOGYM+ePRgzZgwKCwvh5+cHAEhOTsY777yDv/76Cw4ODgb/T01NDWpqarivKyoq0KVLF5SXl8Pd3d3U4RNCCCHkPqqoqICHhwev67dZOSjl5eUAAG9vbwDAiRMnUFdXh2HDhnFtevfujaCgIKjVagCAWq1GaGgoF5wAwIgRI1BRUYG8vDyj/8/y5cvh4eHB/enSpYs5wyaEEEKIhTM5QNFqtZg/fz7Cw8Px0EMPAQCKi4vh4OAAT09PvbZ+fn4oLi7m2ugGJ+z97H3GLFq0COXl5dyfq1evmjpsQgghhFiBdqZ+Y2xsLHJzc/H777+LOR6jHB0d4ejoKPn/QwghhBDLYNIMSlxcHHbv3o0DBw6gc+fO3O3+/v6ora1FWVmZXvuSkhL4+/tzbRrv6mG/ZtsQQgghxLYJClAYhkFcXBx+/PFH7N+/H927d9e7v3///rC3t0d6ejp3W35+Pq5cuQKFQgEAUCgUyMnJwY0bN7g2aWlpcHd3R0hIiDmPhRBCCCFthKAlntjYWGzZsgU//fQT3NzcuJwRDw8PODs7w8PDA7Nnz8bChQvh7e0Nd3d3vPbaa1AoFBgyZAgAYPjw4QgJCcGLL76IlStXori4GPHx8YiNjaVlHEIIIYQAELjNWCaTGb39m2++wcyZMwE0FGp744038N1336GmpgYjRozAmjVr9JZv/ve//yEmJgYHDx6Ei4sLZsyYgQ8++ADt2vGLl4RsUyKEEEKIZRBy/TarDkproQCFEEIIsT73rQ4KIYQQQogUKEAhhBBCiMUxuQ4KIYQQcj9oNBrk5OTg1q1b8PHxQWhoKORyeWsPi0iMAhRCCCEWKzMzE0lJSXqVxv39/RETE4OIiIhWHBmRGi3xEEIIsUiZmZlQqVQIDg5GYmIiUlNTkZiYiODgYKhUKmRmZrb2EImEaBcPIYQQi6PRaDBt2jQEBwdjyZIlsLP7+/O0VquFUqlEQUEBNm7cSMs9VoR28RBCCLFqOTk5KC4uxtSpU/WCEwCws7PDlClTUFRUhJycnFYaIZEaBSiEEEIszq1btwDA4EgVFns72460PRSgEEIIsTg+Pj4AgIKCAqP3s7ez7UjbQwEKIYQQixMaGgp/f39s3rwZWq1W7z6tVostW7YgICAAoaGhrTRCIjUKUAghxEpoNBpkZ2cjPT0d2dnZ0Gg0rT0kycjlcsTExECtVkOpVCIvLw/V1dXIy8uDUqmEWq3GvHnzKEG2DaNdPIQQYgVstR6IsccdEBCAefPmtenH3VbRYYGEENKGsPVAFAoFpk6diu7du6OgoACbN2+GWq2GSqVq0xdrqiTbdlCAQgghbQTVAyFtCdVBIYSQNoLqgRBbRQEKIYRYMKoHQmwVBSiEEGLBqB4IsVUUoBBCiAWjeiDEVlGAQgghFozqgRBbRbt4CCHEClA9ENIW0DZjQgixAGLX76B6IMTaCbl+t7tPYyKEEJsiReVXuVyOsLAwkUZIiGWjHBRCCBEZW/k1ODgYiYmJSE1NRWJiIoKDg6FSqZCZmdnaQyTE4tESDyGEiIgqvxLSNKokSwghrYQqvxIiDgpQCCFERFT5lRBxUIBCCCEiosqvhIiDAhRCCBGRbuXXuro6ZGdnIz09HdnZ2airq6PKr4TwRNuMCSFERGzl18WLF2Ps2LGoqanh7nN0dERNTQ0SEhIoQZaQFtAMCiGESEAmkwm6nRCij2ZQCCFERBqNBklJSVAoFFCpVMjLy+Mqv/bp0wcqlQrJyckIDw+nWRRCmkEzKIQQIiLdbcb29vYICwtDZGQkwsLCYG9vT9uMCeGJAhRCCBERbTMmRBwUoBBCiIhomzEh4qAAhRBCRKS7zVir1erdp9VqaZsxITxRgEIIISJitxmr1WoolUrk5eWhuroaeXl5UCqVUKvVmDdvHiXIEtICOiyQEEIkkJmZiaSkJBQXF3O3BQQEYN68eYiIiGjFkRHSeoRcvylAIYQQNGwPzsnJ4bYEh4aGmj3LIUWfhFgzIddvqoNCCLF5xmY7/P39ERMTY9Zsh1wuR1hYmAgjJMT2UA4KIcSmZWZmQqVSITg4GImJiUhNTUViYiKCg4OhUqmQmZnZ2kMkxCbREg8hxGZpNBpMmzYNwcHBWLJkCezs/v7MptVqoVQqUVBQgI0bN9LSDCEiEHL9phkUQojN0q36qhucAICdnR1VfSWkFVEOCiHEZllb1VdKuiW2hAIUQojN0q36GhISYnC/JVV9lSqRlxBLJXiJJzMzE2PHjkVgYCBkMhl27Nihd//MmTMhk8n0/owcOVKvze3btzF16lS4u7vD09MTs2fPxp07d8x6IIQQIpS1VH2lRF5iiwQHKFVVVejXrx8SExObbDNy5EgUFRVxf7777ju9+6dOnYq8vDykpaVh9+7dyMzMxMsvvyx89IQQYgZrqPqq0WiQlJQEhUKBJUuWICQkBM7OzggJCcGSJUugUCiQnJwMjUbTamMkRAqCl3hGjRqFUaNGNdvG0dER/v7+Ru87e/YsfvnlFxw7dgwDBgwAAHz22WcYPXo0/vOf/yAwMFDokAghxGQRERFQqVRISkpCXFwcd3tAQABUKlWrL5+wibxKpbLJRN64uDjk5ORQzRUBKJ/H8kmSg3Lw4EH4+vrCy8sLTz31FJYuXcqt4arVanh6enLBCQAMGzYMdnZ2OHr0KJ599lmD/mpqalBTU8N9XVFRIcWwCSE2KiIiAuHh4RZ5wbK2RF5rQPk81kH0bcYjR47Ehg0bkJ6ejhUrViAjIwOjRo3iph+Li4vh6+ur9z3t2rWDt7e33pNF1/Lly+Hh4cH96dKli9jDJoTYOLbqa2RkJMLCwiwiOAH0E3mNsaREXmtA+TzWQ/QAZfLkyRg3bhxCQ0Mxfvx47N69G8eOHcPBgwdN7nPRokUoLy/n/ly9elW8ARNCiAWzlkReXRqNBtnZ2UhPT0d2drbF5MdQPo91kbxQW3BwMDp06ICLFy8CaJhGu3Hjhl6b+vp63L59u8m8FUdHR7i7u+v9IYQQW2ANiby6MjMzMW3aNCxYsABLly7FggULMG3aNIuYmaDCfNZF8gDl2rVruHXrFgICAgAACoUCZWVlOHHiBNdm//790Gq1GDx4sNTDIYQQq8Mm8l6+fBlxcXGIiopCXFwcCgoKzE7kFXO2w9KXTyifx7oITpK9c+cONxsCNKx/Zmdnw9vbG97e3khISMCECRPg7++PS5cu4e2338YDDzyAESNGAAAefPBBjBw5EnPnzkVycjLq6uoQFxeHyZMn0w4eQghpghSJvGImizZePmFnKNjlE6VSieTkZISHh7fabI81FeYjABiBDhw4wAAw+DNjxgymurqaGT58ONOxY0fG3t6e6dq1KzN37lymuLhYr49bt24xL7zwAuPq6sq4u7szL730ElNZWcl7DOXl5QwApry8XOjwCSGEMAyTkZHBPPnkk8y7777L5OXlMdXV1UxeXh7z7rvvMk8++SSTkZEhqL9Tp04xQ4cOZfLy8ozen5ubywwdOpQ5deqUCKM3TX19PTN58mTm3XffZTQajd59Go2Geffdd5kXXniBqa+vb6URtn1Crt+CZ1CGDh0KppkDkPfu3dtiH97e3tiyZYvQ/5oQQogIpJjtsIblEzafR6VSQalUYsqUKejevTsKCgqwZcsWqNVqqFQqi8nnsXV0mjEhhNgYKZJFrWU7tJT5PERcdFggIYTYGClmO3S3Q+vOygCWtx3akgvzkb/RDAohhNgYKWY7rG07tKUW5iN/kzHNJZRYqIqKCnh4eKC8vJxqohBCiEAajQbTpk1DcHCw0dkOpVKJgoICbNy4UfCF29jOoICAAMybN4+WT4ig6zcFKIQQYoPYmiUKhaLJZFFTAwo6iI80hQIUQgghLaLZDnK/UYBCCCGEF5rtIPeTkOs37eIhhBAbxiaLEmJpaBcPIYQQQiwOBSiEEEIIsTgUoBBCCCHE4rTZHBRK/CKk7aLXNyFtX5sMUMQ8QpwQYlno9U2IbWhzSzxs8aHg4GAkJiYiNTUViYmJCA4OhkqlQmZmZmsPkRBiInp9WweNRoPs7Gykp6cjOzsbGo2mtYdErFCbqoMiZflmQkjrote3daAZLtIcIXVQ2tQMihRHiBNCLAO9vi0fzXARMbWpAEWKI8QJIZaBXt+WTaPRICkpCQqFAkuWLEFISAicnZ0REhKCJUuWQKFQIDk5mZZ7CG9tKkCR4ghxQiyZLa310+tbGmI9h2iGi4itTe3iCQ0Nhb+/PzZv3mx0jXrLli0ICAhAaGhoK46SEHHY2lo/vb7FJ+ZziGa4iNja1AyKXC5HTEwM1Go1lEol8vLyUF1djby8PCiVSqjVasybN48S6IjVs8W1fnp9i0vs5xDNcBGxtaldPCw6Qpy0Zba+m4Ve3+aT4jlk689Lwo/Nn2YcERGB8PBwqjRJ2iR2rV+pVDa51h8XF4ecnJw2eUotvb7NJ8VziJ3hUqlUUCqVmDJlCrp3746CggJs2bIFarUaKpXKpN8TVQ62TW0yQAHoCHHSdtFaP72+zSXVcygiIgIqlQpJSUmIi4vjbg8ICIBKpTJphsvWcq3I39psgEJIW6W71h8SEmJwP631k5ZI+RwSc4aLzZNRKBRQKpXcjMzmzZuhUqlMDnqIdWhTSbKE2ALd3SxarVbvPtrN0raJtSVY9zlUV1en12ddXZ1FPIeorgqhGRRCrIyUa/3Ecom51ME+hxYvXoyxY8eipqaGu8/R0RE1NTVISEgwedZDjHHq5skwDIPs7Gy9GZm2nmtFKEAhxCpJsdZPLJdUSx0ymUzQ7fdznGz+S2FhIZYsWWIQ8MyaNUuvHWl72uQ2Y0JsBe1uaPuk3hKsUqmQl5fHPYf69OkDlUrV6tuMs7OzsWDBAshkMigUCkydOlUv4FGr1WAYBqtXr6YZFCtis4cFEmJr2N0skZGRCAsLo+CkDZKihLxun/b29nrPIXt7e7P7FGOcISEhkMvl8PT0REJCgl4OSkJCAjw9PSGXy40m+ZK2gQIUQgiB5Z5rJMWWYGvo88yZM9BoNCgtLcXixYv1KgcvXrwYpaWl0Gg0OHPmDO8xEutCOSiEEJtnybU2pNgSbA19soHMu+++i6+//tog1+rdd9/FsmXLKAelDaMZFEKITbP0c42k2FZuDX2ygUynTp2wadMmrF69GvHx8Vi9ejU2btyIwMBAvXZCWeqMGfkbJckSQmyWtZwfo7s7pqlt5UJneiy9Tyl/N5Y8Y9bWCbl+U4BCCLFZ7E6RxMREo8sSeXl5iIuLs4idIlIckmjpfUodRBnbGUTb9KVFAQohhPCQnp6OpUuXIjU1Fc7Ozgb3V1dXIyoqCvHx8YiMjGyFEeqTYlu5pfcpZsBjLTNmbZnNn2ZMCCF8WNu5RlIckmjpfYp5to+tnwRubShAIYTYLN3ETmOfqC3hTBoiXsAj9UngYs9G2XohRgpQCCGSs9Q3WjrXyLZIOWMmduItJfJSDgohRGLW8EYrRbIosTxS5aCInXjblhN5KUmWEGIRrOmN1lJneYi4xN4ZJHbQ09YTeeksHkJIq9NoNEhKSoJCocCSJUv0zlJZsmQJFAoFkpOTLaZAFp1rZBvYk8AvX76MuLg4REVFIS4uDgUFBSYFzGKfQSTF2UvWinJQCCGSoB0TxFKJuTNI7MRbqRN5rQkFKIQQSdAbLbFkYu0MEjvx1tq2vkuJlngIIZLQfaM1xpbeaEnbJfYZRFKck2StKEAhhEiC3miJLWC3qqvVaiiVSuTl5aG6uhp5eXlQKpVQq9WYN28e7+UjsfuzZoJ38WRmZuLDDz/EiRMnUFRUhB9//BHjx4/n7mcYBosXL8ZXX32FsrIyhIeHIykpCT179uTa3L59G6+99hp27doFOzs7TJgwAZ988glcXV15jYF28RBiHaQ4S4UQSyT2VvW2uvVd0m3Ge/bswaFDh9C/f38899xzBgHKihUrsHz5cqxfvx7du3eHUqlETk4Ozpw5AycnJwDAqFGjUFRUhC+++AJ1dXV46aWXMHDgQGzZskX0B0gIaV1t9Y2W3D/WsgWcKsm27L7VQZHJZHoBCsMwCAwMxBtvvIE333wTAFBeXg4/Pz98++23mDx5Ms6ePYuQkBAcO3YMAwYMAAD88ssvGD16NK5du4bAwEBRHyAhpPW1xTdacn9YQ6E/wl+r1UEpKChAcXExhg0bxt3m4eGBwYMHQ61WAwDUajU8PT254AQAhg0bBjs7Oxw9etRovzU1NaioqND7QwixHlRjRBwajQbZ2dlIT09Hdna2xdSQkQq7RBgcHIzExESkpqYiMTERwcHBUKlUyMzMbO0hEgmJus2YjXD9/Pz0bvfz8+PuKy4uhq+vr/4g2rWDt7e3XoSsa/ny5UhISBBzqIQQYlVsbSahcaE/tpYOW+hPqVQiOTkZ4eHhFPC2UVaxi2fRokUoLy/n/ly9erW1h0QIIfeNLc4kUEVV25sxa0zUGRR/f38AQElJCQICArjbS0pKuII4/v7+uHHjht731dfX4/bt29z3N+bo6AhHR0cxh0oIIVZBdyZBpVIhLy8Phw8fho+PD1QqFVQqVZucSbD1Qn+2NmNmjKgzKN27d4e/vz/S09O52yoqKnD06FEoFAoAgEKhQFlZGU6cOMG12b9/P7RaLQYPHizmcAghxOqxMwkhISGYPn06FixYgKVLl2LBggWYPn06QkJC2uRMgi0X+rPFGTNjBAcod+7cQXZ2NrKzswE0PEmys7Nx5coVyGQyzJ8/H0uXLsXOnTuRk5OD6dOnIzAwkNvp8+CDD2LkyJGYO3cusrKycOjQIcTFxWHy5Mm8dvAQQogtYWcI1q5da/SCtXbtWr12bYWtFvqztkM2JcUIdODAAQaAwZ8ZM2YwDMMwWq2WUSqVjJ+fH+Po6MhERkYy+fn5en3cunWLeeGFFxhXV1fG3d2deemll5jKykreYygvL2cAMOXl5UKHTwghRtXX1zOnTp1ifv31V+bUqVNMfX19aw+JYRiGOX78ODN06FDmtddeYzQajd59Go2Gee2115ihQ4cyx48fb6URSicjI4N58sknmXfffZfJzc1lqqqqmNzcXObdd99lnnzySSYjI6O1hyi6U6dOMUOHDmXy8vKM3p+bm8sMHTqUOXXq1P0dmEiEXL8F56AMHToUTDOlU2QyGd5//328//77Tbbx9vbmXZSNEEKkZsnr/TKZrNn72ffjltpZo4iICKhUKiQlJSEuLo67PSAgoM1WIbb13BtddJoxIcSm6ZbjVyqVXDn+zZs3c0morXkhLC0tBQDk5uZCqVQaHBmQl5en104oKYro1dbWYufOnbh+/To6deqEcePGwcHBwaS+IiIiEB4ebjOF/ug047+ZVUm2tVAlWUKIGDQaDaZNm4bg4GC9WhtAQ56DUqlEQUEBNm7c2GoXxOzsbCxYsABz587Frl27DI4MiIqKwtq1a7F69WputyRfUswcJScnIyUlRS9vxM7ODpMmTcK8efNM6tOWWMNz0hytVkmWEEKsiTXU2mCTRfPy8rBhwwasXr0a8fHxWL16NdavX48zZ86YlCwqxU6R5ORkbN261eh9W7duRXJysuA+bQ2dZvw3ClAIITbLGtb7dS9YKpUK9vb2UCgUsLe3h0qlMumCJcVOkdraWqSkpAAABg0apBf0DBo0CACQkpKC2tpaYT8AG8Tm3ly+fBlxcXGIiopCXFwcCgoKWn3J8X6iHBRCiM2ylvV+sZNF2ZkjpVLZ5MxRXFwccnJyeC8b7dixA1qtFj169MC///1vvdL0//73vzF37lxcvnwZO3bsQHR0tKDx2iJby70xhgIUQogeWzp5WLfWhrH1fkuqtSHmBUuKmaPTp08DAObMmQOGYZCdna03zlmzZiE+Ph6nT5+mAIUn9pBNW0UBCiGEY8nbbaXALp+oVCqjO2TYZRVLCdDEumBJMXPUvn17AMBvv/2GTz75xOA59PDDD+u1I6QllINCCAFgu+W1bXG9X4oqrU8//TQAIDU1Fd26ddN7DnXr1g179uzRa0dIS2ibMSGkzW9t5MOWlrYA/fovTc0cCQnOamtrMXLkSDAMAw8PD8yePRsKhQJqtRrr1q1DeXk5ZDIZfvnlF5NrohDrJ+T6TUs8hBBJkiatja2t94udeHvmzBmuqm15eTlWrVpl0IZhGJw5c8amfs62QKrgngIUQohVbLcl4pMi8fbdd9/FunXrUFJSwt3n7++PWbNmYdmyZSY9h2xtdsuaSJm3RgEKIcRqttsS8YmdeNupUyds3rzZIKA4d+6cXju+bC1x25pIfUwEJckSQqzuaHuNRoPs7Gykp6cjOzvbNo6et3C6zyGZTIawsDBERkYiLCwMMpnMpOeQrSZuWwMpiv01RkmyhFgxMae+xU6alIo1faK2hqUJS30OWWPitjX8vsXCnhGVmJhodNY1Ly8PcXFxBmdEUZKsRGzpyUcsn9gXams42t7STx7WZQ2B1P16Dvn7+wv+3Vhb4rY1/L7FdD/y1ihA4cnWnnzEskl1oY6IiMCQIUOwc+dOXL9+HZ06dcK4ceMsYlto4yll3VLqS5YsgVKpRHJyMsLDw1v9g4M1BFJSjrHxxLwpE/W6F0BjHw4tKXHbGn7fYtPNW+vVq5fB70eMvDVa4uFB98k3depUvSefJU19E9sg5dS3JQfipk4p32/WsDQh1RjFfK9kf99z5szB7t27DZ6TY8aMwdq1a+n33UrYx81eixv/fjw8PFBRUWHwuIVcvylJtgX3IxGIECHYqe+pU6c2OfVdVFSEnJwcQf1aekKitWyFlur3IyYpxij2e2VoaCg8PT2xdu1adOvWDa+//jrefvttvP766+jWrRvWrl0LLy+vVk/ctobfty6xEszlcjmeeOIJ5Ofno6amBm+88Qa2b9+ON954AzU1NcjPz0dERIRZQRkt8bTA2tZBSdsnxYXaGpZPrGUrtDUsTUjxHNJ9rzR2WKA575UnT57EkSNHuK/ZJUdzFgDEyim0ht83S8wZUo1Gg4yMDPTq1QtlZWX46KOP9Prs1asXMjMzMXfuXJPfMyhAaYG1fGoj1kGMN0UpLtTWEIjrbmNVqVTIy8vjfo59+vSxmK3Q7M/9xx9/xK5du4wuTei2E0Ksi6oUzyH2PfD69etYsmSJweOeNWuWXruW5OTkoKysDAAgk8n07mO/LisrM+k5KeaFWsrft5jEzpPRfc8wloNy7tw5s98zKEBpgbV8aiOWT6w3Rd0LtbE1b1Mu1NYQiLMnDy9evBhjx45FTU0Nd5+joyNqamqQkJDQ6uv87NLEV199ZXAx2LRpE9auXQtPT0/BgZSYF1UpnkPse+CyZcugUCjw/PPPc7+XrKwsLFu2TK9dS/766y8AwODBg7FkyRKDgFSpVOLo0aNcO77EvlBL9fsWkxQzpLrvGcaK/YnxnkE5KC2wtgJWxDKJmd/BXqjVajXi4+OxY8cOpKamYseOHYiPj4darca8efMEXah1A3FjLCkQb/xpuqXbWxvDMNwfltCxip0fJMVzKCQkBHK5HC4uLrh06RI++eQTrFy5Ep988gkuXboEFxcXyOVyox/0jCkvLwcAPP7440Zn9cLDw/Xa8XE/cgrF+H2LTYo8mfvxnkEzKC1gX8gqlQpKpbLJ4kOt/amNWC4pPr1EREQgOjoa27dvh1qt5m6Xy+WIjo62iE/UusRYmtD9ORpb4lGpVK2eJwP8vTTB7j5pXFNmzpw5WLt2Le+pb6nyg8R+Dp05cwYajQZVVVVwcHBAdHQ0AgICUFRUhLS0NFRVVXHt+DxuDw8PAMBPP/2ETZs2Gcwcubq66rXjQ4qlTN3f965duwzqvwj9fUtBihlSqd8zAApQeLGGAlZEfGKt90vxppiZmYlt27ZhyJAhGDRokN5U+rZt2xASEiLoeSllIC7W0oQ15MkAf7/JP/fcc5g8ebLBc6impgZr164VlIshxeMW+znELrUEBASgpKQE27Zt4+6Ty+VcsMJ3SaZjx44AgAsXLsDLywtvvPEGFAoF1Go1vv76a+75xLbjQ4qEVratn5+f0ZkSX19fwX2KTYpUhfvx4Z0CFJ7EPPWTWD4x1/vF/vTS1CdqABg3bpxZn6jFDsTFXO8XOwlTKo0LWDUm9GIg9a4t3dmogIAAREVFmTQbxS61FBUVQaFQGAQ97CwN3yUZdsnIyckJDg4OBrtEXFxccO/ePd5LRsDfP/Mffvihydoquu2E9Mnm3jR+ni9fvlxwn2KTarZD6g/vFKAIINapn8SyiZ1EJ/anFylnEsQMxMVemtC9EDz66KMGvxuhSZhSYS8Gn376KcrKylBSUsLd5+fnB09PT0EXAyl3bY0ZMwbTp083eqE+fPiwoOeQm5sbAMDT0xOLFy/G2bNnuaBn1KhRmDx5MsrKyrh2LdFdMurbt69e0u2xY8e4gIfvkhGgX1ulqYRWobVV2EDK3d0d7733Hs6ePYvDhw/Dx8cH7733Hl544QVUVFQICqTEJuVsh5Qf3ilAIUSHFOv9Yn96kXrHjViBuNiBlO6FICEhAe3ateNuT0hIQHR0dKtfCIC/C1ht3brV6NJEfn4+nn/++VZ7/gB/PzfWrl1rNNhbu3atXjs+KisrATRs/X3mmWeM7rLSbcd3jO+++y6+/vprvTyZgIAAvPvuu1i2bJlZM2bGElqF1lZhA6nS0tJmH7eQQEoKUs52SPXhnQIUQnRIMTsh9qcXqbe+S1HAyhihgRR7ISgrK8PixYsNfo5lZWVgGKbVLwS6BazKy8v1liYCAgIEF7CS4tOvp6cngIbgx1ggPn/+fOTk5HDt+OCbrMq3Hfv87dSpEzZt2mS0zoZuOz7YhNa5c+caJLSaksAM8H/+mhJIiX1ArVSzHVIdpEsBCiE6pKoKKeanFykLlklRwEqsQEr3E/W6desMfo6LFi0y+xO1GKQoYCX2p9+Wtr2yswhCtsfq/h7DwsLQuXNn1NTUwNHREdeuXcPRo0cN2jWn8cyR7s/K3JmjZ599FhMnTjQ4FLO+vl5QAjOgH+x99NFHBq/HN954Q3CwB0h3LpbYsx1Snt/VZgMUqSI60rZJWRVSrJOCdT9RGytYVltba9J6shQFrMQMpNifeWBgoGifqKUg1RKcmJ9+S0tLAQC5ubmIj4/HoEGD4ODggNraWmRlZSEvL0+vHR9sUNOhQwccO3aMC0iAhudshw4dcPPmTd5LKFLMHOm+vn/66SfcuHGDuy8lJQXjxo3Ta8eHbhDX+OKv1WpNCvas5XRkqcfZJgMUSz6RlVg2KatCGnte/ve//zX5ednUG70p55MY29XBJvqxbzRCc2/EDqSk+EQtBSl2irDE+vTL/t+RkZE4cOCAQR2Up556Cunp6YLGyJalv3nzJjw9PTF8+HAEBgaisLAQ+/btw82bN/Xa8SH2zJHu67uxGzdumPT61g32jAVSQoM9azgX636Ns80FKNYSeRLLxzAM8vPz8eeff6KmpsakT0IsMZ+X7BvDo48+anRmwpRggl2WGDt2rGi7OlhiBVLWUjRRd6fIkCFD9HafHD161CJO4WXH+Ouvv2Lw4MEGyzHp6emCL9TsEkZQUBBqa2v16qAEBATA3d0dV65cEbzUIXbeRHV1NQCgXbt2iIiIQO/evXHu3DlkZmaivr4ed+/eFdQfG8TNmTMHO3fuNCjUNnv2bKxdu9akXXpiH7oopvtRl6hNBSjWEnkSy8Um0UVGRuLgwYN6J6jK5XJERkYiPT1d0ItO7Oel7huDvb29wThMeWPQ3dVhLIhat26dXjshj5s97VR3u62npyc8PT1NqqArZd0FsZ06dUrvOeTo6AjA9FN4pVi6PnXqlN5yDLvkKDQQZ9t7eHgYzcVYuHChSf0C4s0cnTp1CrW1tXBwcEB9fT3279+P/fv3A2i4qDo4OKCmpganTp3CgAEDePXJzuwZO26AYRj89ttvJu3Ss/R6P/fj/K42FaBYS6VJYrnYF1N6errRYlPp6el67fgQ+3kpxRuDl5cXAOChhx4yGkS9/vrryM3N5drxwT7ukpISKBQKvPfee3pBj1qtBsMwgl+Pll40UfcU3qaYcgqvmEvXfMZYWloqaIy6Sx2LFy/m8lr+/PNPbNu2zaS8FrHt3bsXAFBbW9tsMbm9e/fyDlDE3lYu9qGLUrkfB+m2qQDFGk5kJZZNNyN/6dKlBlVaTdl+KfbzUoo3hpY+0bOfeoV88mdLmg8aNAhKpRK7d+9GWloaOnXqBKVSicWLFyMrK0vwabRAwwUmIyMD165dQ+fOndGrVy84OzsL7kcKLZ3CGx8fL/hxi710zf7fPXv2RHl5uV6yqKenJzw8PHDhwgVBY2Sfb0899ZRBXoudnZ1JeS1iY5dvevfubfT1HRsbi3Pnzgla5tHdVl5WVmZQ8VbotnLdCrqXL1/W+zn6+fmZVEFXCnQWj0D3I6IjbZuU2y/Fel5K8cbAfprOyckxmt/BnnIqJMGRLWmu0WgwZswYvRNik5OT8fDDD+u14ys+Ph6HDh3ivj5+/Dh27NiB8PBwLF26VFBfUtA9hdfYEtxjjz2GrKws3o9biqVr9v++cOECt+ykex8bsAj53bB5Lewsoy6tVov09PRWz73x9vYG0HSxuIqKCr12fIi9rbzxoYuNZ2SEHroolfuRE2bXchProfvGrdVq9e6zpCx/YrkaZ+Tn5eWhuroaeXl53Ne67fgQ+3nJvjGo1WqjY1Sr1Zg3b55J2y/nzJmDy5cvIy4uDlFRUYiLi0NBQQHmzJmj144PtiDX8ePH4ebmhujoaMyfPx/R0dFwc3PD8ePH9drxwQYn9vb2mDJlCjZt2oQpU6bA3t4ehw4dQnx8PO++pMI+nt9++83o75sNrvg+bvYCOHXq1CaXCIuKirggkg/dcvNhYWF4/fXX8fbbb+P111/Xu+jxLUvP0k1AjYyMxKuvvorIyEiu6i97cW0tffr0AdCQ3/F///d/eq+d//u//0NhYaFeOz50Z0jZXJnIyEiEhYVBLpcLniHVnd1ydHTERx99hIkTJ+Kjjz6Ck5MTHnjgAb12rYnNCTP2niFGTlibmkGxlix/Yrl0L9TGKk0KzcgHpHleSrH90t/fH2fOnMGGDRuM7gwSGtzr5qvcvXtXb1eH7qd2vnktd+/e5YKT3bt3c8mcc+fOxYwZMzBmzBgcOnQId+/ebdXlHvZ03aNHjxr9fbMJqXxP4ZVi6Vp3ZiQ7O1svSVb3dyNkBkU3AbWurg7p6encbIpMJoO9vT1qa2sFJaCKzc/Pj/t3VlaWXgKzbvCn264lYs+Qsj/zZ555BiNHjjSYkUlNTcWqVasEzzxKhc7iEcDasvyJZWEv1Hl5eaJdqAFpnpdivjHoBlEqlQpTpkyBQqHgPgmZEkRdunSJ+3dzuSuXLl3idcH68ssvAQCTJk2CXC432H45YcIEfP/99/jyyy/x+uuv8x4nS6wdMuxzyMPDA5cuXTLYdtqrVy9UVFSYdFigsSUEU5au2aUMsdoB+gmoumfQAOB2x7DtWitAYX83dnZ2esnGrMDAQDAMI+j1LfaSq+4M3KhRowzq/Qidgbsf6CweASw9y59YrsazHQ4ODrhz5w5cXV3x/fffIysry+RZOCmel2K+MYgdRBUVFXH/fuSRRzB48GCuWunRo0e5T6+67Zpz7do1AA2VSqdNm2awm2XSpEl67YQQc4eM7nNoyJAhmDx5sl6V1iNHjphUoO7TTz9FaWmpXkKrr68vvLy8zFq6fvjhhw12s+jOLPClm1jar18/ODk5obKyEm5ubrh37x6ysrIM2vFVW1trdgVmQP93M3jwYHTq1An37t2Dk5MTrl+/jqNHjwp+fYs9Qyr2DJw1a5MBCiBdREfavoiICPTq1Utv2pvVu3dvi5qFE+uNmyVmEMUmEj/22GO4cOGC3kXP398f4eHhOHToEO+E486dO+P48eP49NNPjW6//Oyzz7h2QrA7ZBr/3EpLS00u7sgGe2vWrNHbheHv7y+4P91trI3duHEDN27cELSNFfg7t8TPzw8FBQUGvxs/Pz+UlJQIykFhl+rs7Oy4YESXnZ0dtFqtoK3qQENCdUpKil4+T1JSEiZNmoR58+YJ6gvQD8R1H7e5s5liBfdiz8BZszYboBBiqvj4eJw7dw729vZcsJKfn4/MzEycO3cO8fHxJu0WyczMxJo1a/QKlvn5+eHVV1816U1R7DdulljBfe/evQEAJ0+eNLjQMQyDU6dO6bVryezZs7Fjxw4AwMWLF/Uu/LqfJmfPns17jBqNBqtXrwbDMHj44YcxePBgvaqvR44cwccffyxacUdTjyHYvXs3gL8v8iz26927d/Pexgr8vUulpKTEYJZHd3ZLyG4W9nfcODGYxd4uJOhJTk42GphptVrudlODFLFnM8Xqs/Esz2OPPWb2LI8uazqnjgIUQnToJmL+9NNPyM/Px61btzBmzBi88cYbeOaZZ0xKxMzMzMTixYsNbi8pKcHixYuRkJAgKEhh37gb7+oAYNYbNyDeGxibaFhdXY26ujpERkbiH//4B86fP4/MzEzU1dXptWvJxYsXuX833sGg+/XFixd5B1jZ2dkoKytDUFAQ/vzzT4OZhKCgIFy5cgXZ2dno378/rz6BpmdlysrKBM/KnDx5ElVVVXBzc0NKSgrOnj3L/W4efPBBTJo0CZWVlTh58iQGDhzIq0/dgO7kyZN6j1t3zK25jKBbLt/T0xNz5szhttuuXbsWZWVl2LZtG2bNmmXyco+lzrJHREQgOjoa27dvN6hmHR0dbfIsrrWdU0cBCiE62ETMwYMHY9asWQYv5EGDBuHQoUOCEjE1Gg1WrFgBoOGNNiwsDE5OTrh37x53gVy5ciXvT+m1tbVISUkB0PCGpfuJlf06JSXFpDfuzMxMJCYmGuQ5xMbGCn4DYwtO2dnZGezqAAB7e3totVreBaf4bqsUsv3yjz/+AABcuXIFjz76qEERtMOHD3Pt+AYoYs/KpKWlAQBeeuklyGQyXLx4kVvSCwkJwYwZM/D5558jLS2Nd4CimyzaOAeovr7epGRRNllXJpMZnSlib2fbteSHH34AwzBo3749Nm/ejNTUVGzcuBGdOnXC5s2bMWnSJFRXV+OHH37A5MmTeY9TKmJe/DMzM7Ft2zYMGTLEID9o27ZtCAkJMalPMYv93Q8UoJA2QaxP/WyC5e+//240z4HNoBeSiHny5ElUV1fDwcEB5eXlOHjwIHefTCaDg4MDqqqqeH8C3rFjBxeUNM7fYL/WarXYsWMHoqOjeY+zqVmeGzdumDTLwxac0mg08PLy4hIn7927hz/++IOrJcO34BTf2jNCatSwP0e26FnjImhxcXE4e/Zsk8sWxujOyly+fFnvE7Cvr6/gWRm2tsipU6fw+eefGyzpKRQKvXZ8NJfXotVqUVhYKDivhd3m3NLhkHy3Q7OvtQceeADjxo0zKPQXEhKCnJwcHDp0qNUDFDHzmJoqzAc0VLs1pTCftZ5TJ3qhNpVKBZlMpvdHd4353r17iI2NhY+PD1xdXTFhwgS9NXlChMrMzMS0adOwYMECLF26FAsWLMC0adOMHt7VkoCAAAANa+8FBQX45JNPsHLlSnzyyScoKCjgEvzYdnywn4Bra2vh6emJN998E//973/x5ptvwtPTE7W1tXrtWnL69Gnu300FKI3btUR3lqcpK1as0LtItKRxwamDBw/il19+wcGDB+Hk5ISePXvqtWuJbuAxZMgQJCYmIjU1FYmJiRgyZIjRdi1xd3cHAL0tsbrY29l2fOjOyjSuVVFeXo4rV67otWtJ3759ATRsO2188WcYhruQs+340M1racru3bsF/b755pYILf52+vRpo49bSGE6KenOmD3yyCN6z8tHHnkEDMPg448/5v2zlKIwnxR93g+SVJLt06cPioqKuD+///47d9+CBQuwa9cupKSkICMjA4WFhXjuueekGAaxAewnl+DgYL03huDgYKhUKsFBymOPPQYAuH37Nrp166bXZ7du3biLH9uOjzt37gAAXFxcsGnTJty9excbN27E3bt3sWnTJri4uOi1a8m9e/e4fxt7QzTWriWnTp3iPoF7eXnpBVFsUFZdXc0ltvKhW3Bq/fr1iI2Nxfjx4xEbG4tvv/0WY8eO1WvXkgsXLuh9nZ+fjwMHDiA/P7/Zds1hH9ulS5cQHx+vV1k0Pj4ely9f1mvHh+4MR1hYGCZMmIAxY8ZgwoQJBjUt+Bg9ejT3bzc3NwQGBsLb2xuBgYF6F3vddi1h81pYnTt3xoMPPqi3A4qd1eOrX79+3L99fX317tPNM9Jt1xzdoHPAgAF6z3PdOiq67VoDO2PGnt8VEhICZ2dnhISEYOnSpQgNDUVpaSmys7N59SdFYT5rPadOkiWedu3awd/f3+D28vJyrFu3Dlu2bMFTTz0FAPjmm2/w4IMP4siRI00+0WpqavQ+4QgpHkTaLinPKAGAY8eOoX379twunmPHjhlt1xJ2VkOj0WDs2LEGU/RCj7dnP83b2dlBqVQiPz8fhw8fho+PD5RKJcaMGQOtVivoU/8vv/wCAHB2dsa2bdu40uRRUVEYMWIExo0bh7t37+KXX37hXWSLLST1008/YePGjXozpdu3b+fGx7fgFPse4ObmhiNHjhjU6nBzc0NlZWWTsyHGNE4W1d0ZpFtRVUiyqKurK4CG98GjR48abFdv164d6uvruXYtSU1N5f5dUVHR5Ptfamoq7yU93T6BppcsU1NTeee16M4I3bp1C08++SR69+6Nc+fO6X1Q4DtzpPt6OH/+PC5dugRfX19cunQJ58+fN9pOCLGWhdnHM3PmTKOzEzNmzMCbb77JO49JijPlrPWcOkkClAsXLiAwMBBOTk5QKBRYvnw5goKCcOLECdTV1WHYsGFc2969eyMoKAhqtbrJAGX58uVISEiQYqjEiuke0tXUtKWQQ7qAvwMPf39/FBcXY//+/di/fz93P1sfQkiA0qFDBwDGZzS0Wi13O9uuJeybqFarbfZTsylVX0eOHMkFJ6x27dph+PDh+Omnn/Sqw7aEvahfuHDB4CJy48YNLmDhe/H39/dHbm5ukwe9sbcb+3DUFDZZtKamxmBpqKamBl5eXnBychKULMrOhNXX1xu9n72d74wZ36W606dP8w5QdIOEprYuN27XEt3fi0ajwYEDB3DgwIFm2zXn7Nmz3L/LysqwatWqFtvxJeaWf74HiPLdYi7FYaD34+RhKYi+xDN48GB8++23+OWXX5CUlISCggI8/vjjqKysRHFxMRwcHAyOqvfz8zNadpi1aNEilJeXc3+uXr0q9rCJFdKdttRoNMjOzkZ6ejqys7Oh0WhMmrZkP8039Xxk39CElJnmm6/Ctx3fbblCzhNhP82zHyJ0f5Z1dXXc0g7fT/1Aw0wW+0ZoLIcAaLgY8t3Fo/vBRox2QEMQ16NHjybzVkpLSxEcHCwo2ON7IeLbzt7eXtR2jf/vgQMH6i2f6M6YCKnbIvbz3MnJCUDT+T/s7Ww7vthk8Ma/89LSUixevFjwsjB7Kvc333xj9LXz7bff6rVriRSHgUrRpy5j779iEH0GZdSoUdy/+/bti8GDB6Nr167Ytm2byQd4OTo6GhwJTgg7Hfnjjz9i165dBtv7xowZo9eOD76FqYQUsOKba8C3Xb9+/bB582Ze7fh67LHHkJubiytXrmDMmDFc4i4AroAX246vnJwcvd1Guhc79mutVoucnBxeU998p/KFTPnX1tZyyzqNz49hv1ar1dwheHyIfc6N7qyVvb09Vz+m8ddCZrfc3NxQVlYG4O/fBftH9+cnJKF1/vz53MX9oYceQqdOnbB3716MGDEC169fR25uLteOjxEjRuDXX39FRUUF2rVrh27dunG/kz///JP7+Y0YMYL3GNmE1uasXr1a0LJwv3794OnpiZycHIwdO9boc8jT01PQ61Gqs7vY6sa6ffr5+Zm1xVjK2iqSJMnq8vT0xD/+8Q9cvHgR/v7+qK2t5V4YrJKSEkHTsoQADdOWnp6e+Oqrr9C9e3e9T4Hdu3fH2rVr4enpKWjaUnda3thSh7F2LTlz5oyo7fj+30LG+Oyzz3L/1g1OGn+t264lugm1Tc2gNG7XnH379onaDmgIbrVaLXr06IH169fDz88PTk5O8PPzw/r16xEcHAytVosff/yRd5+3b98WtZ3uLietVosXXngBGzduxAsvvKAX1Aqp/xIYGMj9++jRo4iLi0NUVBTi4uL0cmZ027XEy8sL4eHhAIDc3Fzu8MC9e/dywUl4eDjvhGPd3Z/19fW4ePEi8vLycPHiRb3nNt9KxEDDkhV7/WnqeV5WViZoaUsul3NBkm7wqPv1iBEjBM9OREREYNOmTVi9ejXi4+OxevVqbNy40ayL/pkzZ4wWOeT73tOY2JsUGpM8QLlz5w4uXbqEgIAA9O/fH/b29nrFmvLz83HlyhVuL79YpJhykmoai4hD91MgS2gC3a+//sr921hei7F2LeFbn4Jvu+3bt4vaDmh4k21picDe3l7Qm6zuJ6rmgr3mlnd18S1HIKRsAXvhLCkpweTJk1FSUoJ79+5xX7MF69h2fPDdPSVklxVLo9Hgu+++w4svvojvvvvO5PcgducY0HzwqNuOj6VLl3JBSmPh4eGCjohYt26dqO2AhiVMMdsBDb+TjIwMeHl5GcyCsmcPZWZmmvS7YqvdRkZGIiwszKwaJWz1aQ8PD71deh4eHti6dSuSk5MF9dd4k4Lu7qUlS5ZAoVAgOTnZrOuk6Es8b775JsaOHYuuXbuisLAQixcvhlwuxwsvvAAPDw/Mnj0bCxcuhLe3N9zd3fHaa69BoVCIulVMiiknaysRbOnEyKDPyclBWVkZ5syZg927dxtMhc6ZMwdr164VlCSr+/ttbiaB70UV4L/jh287to6GWO2Ahh0sjT/9NVZXVyeonLquxrM5QmZ3WHyX6oQs6bH5C00lrLK3C8lz4PuGzLddYGAgt93Z29tbb+ZF92shsx0PPfSQXvJ3c+2EWrp0Ke7evYsVK1YgIyMDTzzxBN555x3BS/x8cw2F5CTqvm51ly4bfy3k9c0m6wPGlzLZXBch70Niq62txfbt2+Hl5WV0lx5bVl9I9WkpNik0JvoMyrVr1/DCCy+gV69eiI6Oho+PD44cOcJl6q9evZqrCRAREQF/f3/88MMPov3/Ukw5ST2NZWvEKqzGJr8+99xzRqdC2eUIIUmyuomlzc2gCElAFTtAMXb+jjntAGmWT8TG96Ih5OLy6KOPitoOEH+JR7e2ze3bt9G1a1eMHj0aXbt21evjgQce4D0rM3z4cC7ocnFx4eqW+Pr6crMmTk5OGD58OK/+GnN2dsaUKVMAAFOmTDEp/7DxhZKdDW08KyrkOAfdoL25GkJCgnvdGbvGs5C6X7dmQdKdO3dCo9Fg9uzZRmczX3rpJWg0GuzcuZN3n/ejtoroAcr333+PwsJC1NTU4Nq1a/j+++/Ro0cP7n4nJyckJibi9u3bqKqqwg8//CBa/okUU073YxrLlogZ7Onu7Tc2FWrK3v5u3bpx/3Z3d8cbb7yB7du344033tDbTaDbriViL/Hwfb0IeV0VFhaK2g4QPzlYik/U33zzjd7XAwcOxGeffWYwS9S4XXPEDkh1i6cBwP/+9z+kpqbif//7n97t+/bt431hdXFxwaJFiwA0FGRjl7Ju3LjBFXBbtGiR4CUeMTW+2LMzE42XpITsXrp7965Bn42XhY21a05eXh737/79++u9r+kmf+u2u9+uX78OAE2mUrC3s+340H3/NUaM2iqS56DcT1Qi2LKJHezp7u03tr3PlL39f/75J/fvsrIyfPTRR5g4cSI++ugjveRu3XYtaWnpRGg7KWZQGm+5tLe3h7u7u8Gbv5Ay8o2T4c1tJ0VuR+M35GPHjuG1117TK8pnrF1z+NY34dtu2LBhLW7vdnNzQ1JSEoKCgnj1CTQkYSYkJBit+ir03CUp6BZjAxryMaZNm2awFNy4XXPat2/P/fvEiRN6ycG6VXN127Xk5s2bABqCvvj4eJw5cwZffvklzpw5g/j4eC7IY9sJIVbeY6dOnQBArxChLvZ2th0fUrz/NtamDgukEsGWTew1S3Zv/+LFi5vcGpuQkCAot0WKREyxd93wrZQqpKJq4y2vdXV1RgMmIVWc+b4h822nO7XfXC6GkMRo9pOzs7Mz3Nzc9E5x9vPzQ3l5Oe7duyeoHojYM0cuLi546623sHjx4ibzJt58801Bu1lYERERCA8PR2pqKlatWoWFCxdi9OjRFnFgXOPXg0ajwaZNm1ps15z+/ftzRyE0fn7r/lz5nlwN/P06q6qqQlRUlN59iYmJBu34ErOY3Lhx45CcnIx169ZhxIgRBjsSv/nmG8jlcowbN453n+z7r0qlQlRUlMH29/r6eqhUKrOeS21qBkWKKaf7MY1lK6QorMZqLqFVCCmWT8Qmdp0NQPxlKED8YE/30+Pt27fxyCOPYPbs2XjkkUf0ghUhnzLZT7d3797FW2+9hYULF3KJ/G+++SY3G9OaSx3A37Mdjbfoent7mz3bIZfL0atXLwBAr169LCI4AfQD14EDByIiIgIPP/wwIiIi9JbghMxM8E3wFpIIzv7sxGoH/F1MrvFro6SkxKRicg4ODpg4cSJKS0sRHR2NXbt24ebNm9i1axeio6NRWlqKiRMnCsrnYTEMY3R7tZCgviltagaFSgRbNrELq+kWXRo0aBCcnJxQWVkJNzc33Lt3D1lZWYKLLj3++OO8thA//vjjvPoDDDP7m2vHh27RQvZcF2NfCyluKHb1U0D8mSO5XK4XfJw8edLoYXZCLrB9+/bF4cOHAQBvvfVWs+34alw6vrl2QljybIfUjh8/rvfcE/qzYz300EMtvh5lMpmg3Uv9+vXDd999B6Dh9RcREaF3BhH7/OZbqE2j0WDlypUAGuqIzZkzBwqFAmq1GmvXrkVZWRlWrlwp6H0NAObNmwegofyA7rEBcrkczz//PHc/X3xPQBc6Tl1tagZFt5xvfHw8duzYgdTUVOzYsQPx8fFmlwgWq09bJXZhNbboUocOHXD8+HFkZmbi1KlTyMzMxPHjx9GhQwfBRZf27NkjajtA/O2xuhc/Y3UXmrqvOVLktej+/42DL92v+Y6Tb/Ved3d33nkob775pqjtgIbdNGK202Wpsx1S0M1/ahxQ6D5n7O3tef++c3NzWwyyGYYRVPeG3QIONATb+/fvx5o1a7B//3694Fu3XXPYk6bd3NywefNmvdPPN2/eDDc3N8EnTbPmzZuHbdu2oVu3bnBzc0O3bt2wbds2wcEJIM0J6I21qRkUoOFTRnR0NFJSUvQSguRyOaKjo00uEczuExerT1vHMAzOnz+PP//8E7W1tbwP3NLFPvFv3rxp9MLJTv2eOnVKbwthc/Lz80VtBwDvvPNOs5/OddvxoXsOUHMBiqOjI+7du8erhocUn/p1ZzyaKwbG9yL79ttv8/o5lpaW4sqVK/jHP/7RYlu2+umhQ4eabCOk+ikAxMTEYMGCBbzakaa99957UCqVLbarq6vj/ftufAJ2c+34vmeIXSk6LS0NANCzZ0+jp5/369cPp06dQlpamuCaRDExMTh37hz3dWVlJSZMmIDevXsjKSlJUF/sh7T27ds3ewL6nj17eJ+A3libC1AyMzOxdetWg2QyuVyOrVu3IiQkRHBAkZmZiW3btmHIkCEYNGgQd75CVlYWtm3bZlKftogtrBYZGYmDBw/qvVnI5XJERkYiPT2dd5Ks7nT/oEGD8OKLL6J79+4oKCjAxo0buf6Fbitn+fr6GiRNsmvCQvp86KGH4Ozs3OzWRWdnZ97Tyo0P22wKW6WZzxu3FOfcuLi48MqD4Zvfwffn+PHHHwvazbJ06VLEx8cbDVKEVj8FGkqvd+zYsdnS8x07djQpqdWWDBgwQG85hv1349uSk5N5/75///137t87d+7EpUuXuGKRPXr04JJEf//9d7z66qu8+uRbxI9vO3ZWwtgMiVar5T6YCckHAwyDE13nzp1DTEyMoCCFnREaMWIEZDIZsrOz9Qpvsieg8505MqZNBSi6OQn9+/fHtGnTuAvWpk2boFarBeck6G6NValUyMvLw61btxAQEICoqCioVCokJyebtc5mK9jk1/T0dDg4OOhd5OVyOXcEAt8kWXabpqOjI5RKJVJTU5GWloZOnTpBqVTiueeeQ01NDe/tnI19/fXXuHDhAvei69mzJ5cnI4STkxNSU1MxduxYo2NxdXXFrl27ePf3+uuvIyMjo8V2n332Ge837p49e/L6hNezZ09e/QHA559/junTp/Nqx4fYP0ddYlU/Zce5bds2TJs2zej25E6dOhndjUL0OTk5Yf/+/YiMjIRWqzWog2JnZ6d3bAof7Kyqk5MTXFxc9D4IabVaODk54d69e4ISbyMjI3nlrUVGRvLqLyQkhAuWPTw8MHfuXC4H5auvvuLq5/A9BRxoeK9sKjhhnTt3Dnfu3OF9Yjn7weK3336DWq02yClkE2fNSTBvUwEKm5MQGhqKhIQE5OXl4fDhw/Dx8UFCQgIWLlyI3Nxc/PHHH7yn79itsWPHjsWLL75osOVr7NixOHz4cKuWMbYWup/8mwogG7drDrtzo6amptntfXwrdjY2ZswYDBo0CE8//TS+++47ZGVlmdQPa9euXfjrr78QExPDBT1JSUlclWW+dJcl2rVrh4CAAFy9ehVdunRBUVER6uvrER4eLijRLyEhAZMmTeK+Zpd8Gi/9JCQk8O6zS5cu3Bt+U306OTmhS5cuvPsExPs5NsZWP83IyDC5+qmuTZs2oby8HG+88QYuXbqEHj164KOPPtJboiMtS09Px9WrVzF79mzU1dXB3t4e69atE/y80XXv3j3Mnz8fo0aNQqdOnXD9+nXs2bPHpPOR+NZE+vPPP3kd6aK7/PnAAw9g7969SElJgbu7Ox544AHunCAhCevLly/n3e7f//43r7aPPfYY8vLycPPmTXh6eiI6OhqBgYEoLCzEvn37uPpGQk5Ab6xNBSjs1Ff//v0xffp0g4huxIgRyM3NFZSTwH6a/+qrrwzuKykpwdq1a/XakaaxFyU3Nze8//773JplSEgI3n//fTz33HOorKzknTTJ9wIi5ELToUMHvWWJrKwso4FJhw4dePepq2PHjli2bBleeeUVLFu2zOSLqu6yBFs5lf3blGWJDh06cDsPgL9/V7q/i969ewt+3Hv27MGoUaNw7949gz6dnJwEJRvrEuvnKDUPDw+8/fbbeOWVV/D2229TcGKiLl264PPPP8crr7yCzz//3OTgJDAwkKvAm5OT02SBTSHnGvFNwv/jjz8wefLkFtvpLn03d2jhkSNHMHXqVF7/96VLl7h/N/7Qofu1bruWPPPMM/jiiy8ANBRb3LZtW5PtTNWmAhR2fXz9+vUYPHgwwsPDUVNTA0dHR1y/fh0bNmzQa8eHbnJcU1u+GrcjxrFvBnfu3MF7772HQYMGcblCWVlZ3LR9Tk4Or+QvsadWAWDZsmW83kSWLVvGu0+piLksATQk4DW1Tm1KEh1rz549KCkpwcsvv4yKigq4u7vjyy+/FHSeESFiWLFiBa/Xd0vbZ3VJcXin2HR3Ew0cOBBDhgzhcimPHDmCo0ePGrRriZANBaauLrSpAIXdntquXTscO3bMIAmzXbt2qKurE1SzhF1HY5Ns2UI2UVFRePrppzF69GhoNBreZcptGTslOXToUGRmZhrsiHriiSdw8OBB3lOXfKP9S5cu8T4t28/Pj5vybUqnTp0s5uIq9rJEUlIS7ty5g/j4ePzxxx/o168fli5dyntduil+fn748MMP8corr+DDDz+0mJ8fsS1SvL7FPntpwIABvLY5C9kZ4+Liws3yX758mQtIAOjNPgrJF2GTwD09PY0eV8He3lyyeEvaVB0UdgtkXV0d7Ozs8MILL2Djxo144YUXYGdnxwURQrZKsklYGo2Gy2uprq5GXl4eEhISuERPocla1kSs8yAefvhhAMCBAwfQv39/vcqQ/fv3x8GDB/XatYTvz1zo72bTpk1NnklhCwmOrq6u3A6GV1991ezghLRNJSUlOH/+fJN/2BmDK1euNNmmNU74Ffv13bhqdfv27REXF2dwng/f6tYtJbMKbQfoX/MaBwy6Xwu5NrIBV1lZmUEFWgcHBy5o4RuYGdOmZlAaZ15/9913XIU/3aI/QjK02a1cU6dORXp6OuLi4rj7AgICMGXKFGzZskXwli9rkZmZiaSkJIN8npiYGMFbqx966CFuvbOphFM7OzveyZ1SnEnDogRHQppWUlKC6TNmoJbHa6u5pEsHR0dsWL/+vs+oifn6ZmfmWdXV1UZ3pumef9Mc3fdaMdoBDceL8EnmberMOWPc3NxEbWdMmwpQ2Ijysccew4ULF/Sic29vbzzwwAM4dOgQzp07hxEjRvDqs2/fvjh06BCOHDmCDRs2cNuMfXx80KdPH64Cn5BS2Lo0Gg1ycnL09o9bynblzMxMqFQqKBQKKJVKbsfN5s2boVKpoFKpBAUpZ86caTEBVqvV4syZMxaxI4oSHElbUlJS0uSnWd3ZjqZ4eHhwgUR5eTlqa2rQ9/FX4erJP6FU152yQpz+bQ3Ky8tbZclPrNe32Kdsi31EBAAEBwfjwIEDvNrxpbus0759e4wfP15vFw87Y8T3tHJj2lSAwuYuFBUVGb2fjTiFbM8aP348vvjiC1y6dAnvvfcepk2bBoVCgYKCArz33nu4fPky7OzsMH78eMHjFXN2Qmy69V90zyAKCQnBkiVLoFQqBdd/YQPG9u3bo3379nozWR07dkRVVRWqq6t5T/tK8UmDEEtgbjAB6AcUfGc8hM52uHoGwsOH/6futkjsc6waFzd0c3PjdthVVlY22a45fIulCSmqxv7/9vb2qKio0NvFY2dnB3t7e9TV1QkaZ2NtKkDp3LkzgIakSGP7stkLH9uODwcHB0yaNAlbt25FVlaWXuIte9GeNGmS4FMg2dkJ3aUnoKFmhymzE2Jj678olUqDdUk7OztMmTIFcXFxguq/sDNcjzzyCC5evKh3n1wux8MPPyxohkvsY+0JsQRiBBOAfkDBzni4PD4Tcg/hJ3FryotR9du3rTbbYcnat2/Pa4m/cU5KUxoXIRw8eDAmTZqElJQUvV2LQgpQShGgsFW26+rqDHbG6p5wrFuNW6g2FaBERUUhMTERdnZ2BvuyZTIZl//QuKhXS9hlnJSUFIP7TD0FcvXq1VzZZl1sGeePP/64VavTshnfTa1JsrcLqf/CfoL4/fffMWTIEDz//PPcVrejR49y1RP5ftKQ6tRYYjnEnkkQo08h/ZnSJxtMOD4+HnYeptXb0ZbfRM1vOwwCCrmHP9r58D8GgLRszZo1mDlzJq92fDR+T/v111+NllMQ8sGL7yyGkNkO3d0/zZ21ZU6NojYVoLD7srVaLdq1a4fu3btzdTYKCgq4NTtT9mWHhISgQ4cOetFghw4dBJUbZmVnZ3PrcmFhYejcuTNXr+XatWs4evQoSktLkZ2djf79+wvuXwzsyboFBQXo1auXQZ5MQUGBXjs+AgICuH+fOHFCbzZKdyZJt11z/P39UVhYqNeHv78/iouL9ZLW/P2Ff2Ikwol9oZZiJkHspQ4pk0XtPDpA7sPvtdBWSBGQSq1r167ccgZgvGKyvb09unbtKrhvZ2dneHt7o76+Hu3atcPt27f1zqLiexio2MtQQMOp4WK2M6ZNBSjsdil2yu3ChQt697O3C92XzS7HNF7GKS8vN2k5hq146+Pjg2PHjuntSbezs4OPjw9u3bqFU6dOtVqAEhoaCn9/f3z66acoLy83yJPx8PBAQECAoJoyPXr04P7duG6M7te67ZpTVVVl0AdbTbW5dtbI0mcSGi7U01Fb0/JWyuYv1A7YsH6D3rKE/LHBkHmY9ibHlFeg9vej3EwC12f4E5CZkBTJlJej9lCGQX/2jw2HzMPbxDHeRu3v+6xy+eROWWHLjXh+b0lJCWbMmNHirruWAlJHR0esv887g/bt24fhw4ejrq7OoGKyvb099u3bx7svV1dXbvnm7t27zdZs4XsYKN/3QCHvlXyTXylJ9v9j32yrq6vh4uLCnQPC/s3+8IXsy9Zdjnn44YcxePBgvWWJI0eOCF6OYXNhbt26BS8vLzz99NNcrkxaWhq3bNIaNQJYbOG0rVu3wsvLC0OHDuV+jn/88Qfy8/Px/PPPC1qCKi0t1fu6qeSvxu2aIsW0pRikmUlo+eLf8kzC3xd/Mfo0DCZqYff4w5B5mFY3hSm/g9rfThlcqGUe7pD5mHbxb4rMwwN2PsKXT5qaVJd5eMPOx9eksTTVp7acfzkEvt+rKTctYbyp7zv9G79lCz7Ky8tRU1ODYYpYeLsbr1PSktsV1/GrOrFVgr19+/ahqKgIc+bMQXV1Ndq3b4+1a9fynhFmrVmzhtchmytWrOB9GCjfQqJCCo7qfrBuqZ3QNAhWmwpQdKvgVVVVcQFJ46hQSLU8djkmKCgIly9f1luW8PX1RVBQEK5cuSJoOYY9z0Qmk6Fdu3Z6uTIdO3bk8lBMOe+ltrYWO3fuxPXr19GpUyeMGzdOcAIv0BCYZWRkwMvLC6WlpVwRNZaXlxcyMzMxd+5c3kGK7nIMAFRWVuLYsWMttjMXwzC8p0LNJd1MQi1kTwQCHsJ/lwCA8lrUZhQ2+uRfC9njvSHz5Je8p4spq0btb+eMBBOukPl4mjbGpv6vctMDzKa+lykvazIwaL6/MqO3a8tNO5Cyue+t+W2HyX02peq3b0XtT4xtxo15u3dCR2/xdgaJnXPUnICAAKxevRqvvPIKVq9eLTg4AQwP2TTGyckJgwYNEty3mIQckmiqNhWgnD9/nne7UaNG8WrLHgRl7Amsm4/yxx9/8A5Q2IxvhmEMisbdvHmTWwcUWvwtOTkZKSkpeslTSUlJmDRpkuAIlt3F0xR2lkPILp7du3fzbjdjxowW23l6evKebeE7FWou9sLvEQG0M7FsSn05UJ5Za/gp0MMBsg6mlbJvamVZ5tkeMh/TCikZ65Mp57+zgO/3an7n90lNCM2hTFH7q/+d/xQ+X2IkyTZm7i6exix9m3FJSQlmTJ+BmlrTl40cHRyxfsP9XTLSPWSzMXMO2bQ2bSpAkWIai29Zd1PLvzeX/SxEcnIytm7danTHytatWwFAUJDCd3lJyDKU2Ougn332GaZNm9Ziuw8//JD3VKhY2nkA9h34H0qpz7TngCXQ/nZK9D7NzUExFuDIwyMg8/A0ob8yo8FNu8eGw87EHBRt+W2jAY4USbK2tounvLwcNbU1mBD2Kjq6CZ/p+auyEP/Nbp1icnTIZhsLUE6fPi1qOwC8lwWELB/wPU2Zb7va2lpuC3T//v3h7OyMyspKuLm54e7duzh27BhSUlIwa9Ys3ss9fA6rYtvxrcrr7Oysl4HeXDs+OnXqpJdQZoyrq6ugQ7VsDVNm2hENTX2fuTkoxgIcaXJQPEXNQbEzIweF6CutaDop1NTv7egWiEAPy53paYqtH7LZpgIUvjU5hNTuyMjI4N3uxRdf5NWWb84G33Y7duyAVqtF+/btjeZ0sLuXduzYgejoaF595uTkcP92dXWFn58famtr4eDggJKSEi4o0G3XErEfNwDs2rULY8eONRqkuLq6YteuXS32cT/XqM3BlAk/U6il72V+OyfqfI0UOSjEtqSpE1t7CG2OUqnEkiVLeLWzJG0qQNHN2WjXrh2effZZbnfMjz/+yNVBEZLb0dwWL1PaATBIOG2u3WuvvdZiO3ZGqKnHxd5++vRp3gGK7jLLnTt3mpylELItje/2bqHbwHft2oW//voLMTExXK2WpKQkXgWCxN7NwqovA0xdqmn4XiMyi0Rf/DEnSZb5jf9pquaQIklWbEz5bZOSbtnvNcYadvGIuc2Y9bQiFl4m7uIprbgueYAj9i69++HRRx8Vtd390qYCFF319fVGK78KpZuk1LFjR72Lp6+vL5coy/cgKED8/eOOjo6itgPAeynIlB1CUujYsSOWLVuGV155BcuWLeNdvZBNau35KOBsQlLr3XLgwmHDhNby34T31aKIAMg8+f8OdTFlNUCm4RlVlpwk6+HhAQdHR9SamSTr4OhocBAcU15u4i4e/QvT32M0L0lWd4xsn+bu4jHWpzm7eIz1Z+42Y2O/Gy+Rd/EAwF93TAukGn8f36RbwLISb52cnHDgwAE8+eSTTbbhc5jg/dZmAxQACAoKQocOHXDz5k2DaNaUbafdu3fHlClTuOq0arXapHMGGpcodnR0RK9evZCfn69XpIhvKWMp6oGwM0982pmiX79+uHHjBu7cuQNXV1f4+vpyO6Zag7MH4CpimoPH40A7T9O+t77MeIAj83Q0eRcP0ERAIVIOSsMFywG1ZibJOjg6cBcsPz8/bFi/vtnlt3//+9/4v//7v2aToHU/rXIBxSF+S7fGx+jIe4x8x6k7Riketxh9CunPlD5Zt83IQWnqe/97Spx6LWzS7awH5iHA2bT3vqK7hfj6YrLeh5r7VUH3wIEDuHTpEubOncsdtfLVV1/xLo6pa82aNXj11Vd5tTNVmw5Qrly50uQvle+2U7YoGwBkZWUhKyuryXamkMlkqKmp4ZZp+J4vo0tIQitfUuzi0X1susFIZWWl3gnUbeHsnHaeEuziKa81fYmnXH8J6++AwvQcFMNgYoPoFyw/P78W33SDgoJ4byEX+0LNd4ymjFPMxy1Fn2I/bg8PDzg6OuJXM5doHI3Mykx4+FV0dDVhF8+dQtGCm6aIsRUa4D8r06NHDyQnJ+OVV15BcnKyScEJADz44IOitjOmTQUoX375JV5++eUW2yUkJPDedjpt2jSsW7eOVztTNN5WbMrJu3yXl4QsQ+kGDGK0A4BevXrh7NmzvNrdb9X8iwuL8n1CcMFEhnkF7IQEFJZyoZaCFBd/Yj4/Pz+sF3nmiGNqFN7E9319MdnEDg2xszLzur+IQCfTlnwK75UguWDjfd8OLfWyUZsKUHr27MlVYW2KTCYTdG7Os88+yytAefbZZ3n3aQ3YhGIABj9T3a9127Xk/fffx6RJk3i1u98uHha3v/pywOQk2Ubvz2IEE4D0sxOEmEvs56SHhwccHRzx32zTZ0EcHQxnZMRY4jFgTga8ke+9X7sTxVw2aqxNBSgAsH//fjz11FNGgxSZTIb9+/cL6s/FxQUJCQlYvHhxk20SEhIElc9nc1j4tDNVz549DQ5LNJVYxeQ6dOjAnb3TlN69ezdb4l+qtdoHHgXam5AkW12uH9ywsx3lmS3/fpujO9sBUDBBiCn8/PywfoO4S3oAEOAciCCXbqKONfnPjaL1db8r6Iq1bNRYmwtQgIYg5X//+x9mz54NjUYDuVyOdevWmXTcNQBEREQgISEBn376qV4NFR8fH/zzn/8UNCMDNCxFzZw5k1c7U4kVnIgtKSkJMTExRoOU3r17IykpqcnvleLQPFZ7kZJkpcrFIISYRorgvuiu6cutTX3vvG4vItDZxCWeuyV6AQ67bBTzwHgEOgsvSFh49yaSLu5o9RO222SAAgBdu3bFmjVr8Morr2DNmjUmByesiIgIhIeHIzU1FatWrcLChQsxevRoQUXFdMdmb2/fbMl9e3t7s8dsDikztJOSknDnzh3Ex8fjjz/+QL9+/bB06VK4ujZfgZTdEhw2CHAzrfI5KiuA7Cwj59yIyFpyMQghwrBLRubmoBhbNgp09kM3ly5m9dtYoHMHdHcV57gEseq/8K0UDrThAEUKcrmcS+Ds1auXScEJa9++fRg+fLjRIMXe3h779ol/+BjAf3u11Bnarq6uePXVV/HKK6/g1VdfbTE40eXmDnh4mfTfEkKIyVpaMgJMnyEtvMd/R2RjTX1v4V3Tiv01/j4x6798nvg573FYfYByv/aPS2Hfvn0oKirCnDlzUF1djfbt22Pt2rUmHdHNl5BTfa2xsI+p7pq4G8fU7yPmqa2txa+//goA+PXXX9GtWzeLKRqoy1rGSfiTZHu1gyOSC8zLQTE2K5N0cYdZfbLYJaNXew1DYHvTPh0WVpdiTf6vgupxWXWAcuPGDcTFxqKmhYTTlvePO2D9hr9zEu7n2SwBAQFYvXo1XnnlFaxevdqk4OQ///kP3nzzzRbbvfHGG4JP9TUnQ1uqktB3zKhe3vh72aTWC4dNT2ptnNBKDGk0GuTn5wMA8vPz0aNHD5NnIJOTk7F9+3buBPGUlBT88MMPmDhxoqATu6WWnJyMlJQUrnRASkoK/vvf/2LSpEkWNU7SusRI5AWMv1dO7DwUHZ08BY/pr3tl2H7toODvE5tVBygVFRWoqa3Fqw8ORCcX08p1X6+qxJqzx7ichIaprOlmBT1CAh7A/KCnf//+zY6VNWbMGF7tGjMlQ5vvzxEQ9rMEgFPGa+WZRIp6INZOzGACADIzM5GUlITi4obzXFatWoUtW7YgJiZGcIJ5cnIytm7dCi8vL0RFRWHTpk2YNm0afv75Z2zduhUALOLiz47TWNFBSxonsQxSba82J8gwNiOzJv9Xk/szhVUHKFJomMqqRUxIEAJdhJXCB4DCqntIOnNFcMADmBf0fPHFF3jllVea/P4vvvgC58+f17ut8YVVzJkj9uf46ADAw7TYEeWVwOHjhgmtDw8CXE1Mkr1TYRjg0Bbev4kZTLD9qVQqKBQKzJgxAytWrMA777yD3377DSqVCiqVine/FRUVSElJgZubG5YuXcodxdC1a1csXboU//rXv5CSkoKIiAgEBwcLPspCLBUVFdi2bRsAICQkBAMGDMC3336LmTNn4vjx48jNzcW2bdswZcoUuLub+EQmpBlSba+e1HUQOjqZ9pz9614FUv4n7NNlmwhQ1pw91tpDaBJ7oR7Vox18nE0rfX7rLoM9l2pNCnpYxoIX3aBHipkjqYhcz4igIXl6x44d+PLLLxEaGoqRI0dyF9WTJ09CpVLh5Zdfxvjx43lf+KuqqvDpp58iNDQUU6dOhVqtBtBw8vfUqVNx584dfPbZZ+jfvz+vOkKbNm2CVqtFZWUlYmNjudsbPydjY2PxxRdftFpAuWHDBq5WUG5uLnfExLfffsu1YRgGGzZsQFxcXGsMEffu3dP7sNHUB5CgoKBWC/SIecT84MXOyAgNMBpzdHAUFJS3iQBlUrcQdHTmXyhN1193q5Dy5xmD25PONJ9YK9SeS/wrrraEDXqe+IcdPFxMC3rKqxhknP876GH7HNQbcG8vvM+KagZZ5wxnOw4fN2l4RrH5ItlZ4hZBs2ZiJWH++eef+OKLLwAAp0+f5s6G0r2ofvHFFwgLC0Pv3r159bl//37cunULt27d0gsoNm3ahE2bNum1Gzt2bIv9sUc1rFy50ujvr6ysDO+88w6GDh3KO9dKigs1O7MzcuRIHDt2zKB20oABA7B3715eh3EKGaOQcV65csXoh5bGwV5rBnq6xFp2pMDMNGLuXrKZbcbu7u5wdHAwGmAI4ehgeMGa2N0fHZ2Fv9H/dbcW2wuKDW43fwbFMMDJOG/KgfHNyzoHiDnP0PdBwNW02BF3qoDTOkf3SFXy3VqJmYTJZta/8847CA4ONrj/0qVLWLlypaAMfHt7ewANFZwfeughPPTQQ1z9H3ZmgWEYrl1L2N/pjRs38MgjjyAnJwe3bt2Cj48PQkNDkZqaCgDo06cP74uLFBdqdsv8L7/8AoVCgUGDBnGHjmZlZWHv3r167cQao5BxBgUFcQFpS+34Evviz/Z38uRJbN++nQv0Vq1ahfXr12PixIl45JFHBPVpbYGZFO7evYstW7YAALZs2YJ33nmHV9Ag1u4lm9nF4+vri/UbxL1gNUxlORgNMvjSDXjY/vZcMu9Tf+M+HeztUdtMoTc+HOztDQKzYY/I4OUmPJAqrWTw68m/Axv2cZ8+K97jBihfhCV2EmZlZSUA4IknnoBcLsfOnTtx/fp1dOrUCePGjUPnzp2xcuVKrh0fvr6+AIAuXbrgxo0b+O6777j7/Pz80LlzZ1y9epVr15Jx48YhOTkZSUlJWL9+Pf766y/uvo4dO6K6uhpyuRzjxo3jPUb2Qn3y5Els27YNpaWl3H1eXl6Ijo7GI488IuhCHRkZibS0NNjZ2eHChQvc0hbQcNwDe6p3ZGSk4DGmpKTg9u3b3H3e3t6YNGmS3oWaDycnJ+41Ultba/D7NmUWTuyLf1P9AcCtW7f0AiyhgVlLP0uhux1ZYm8rF7u/+Ph4HDp0iPs6IyMDGRkZCA8Px9KlS03uVyqtGqAkJibiww8/RHFxMfr164fPPvsMgwYNEtSHFEeImxv06AY8LfVnap8bNm606JkEKR43aaCbhNn49Gv2623btglKFvXx8QEAfPTRRzh48CC3hRdoCIaGDh2q144PNg/D2FJESUmJQbuWODg4YMiQITh06BCqqqr07mODlfDwcEFv4E5OTiguLjY6m1BaWoovvvgCCQkJggJedkZIq9Xi5k39gle6X/OdOdIdo6Ojo959VVVV3BhNSWJuvGWbvc2ULdvsxX/79u1IT0/Xe27a2dkhMjISEydO5H3x79SpE3x8fGBnZ4dbt24Z9Ofj4wOGYbBkyRJBgVlTv+/bt2+b9PtmZ3oaP252RlP3cQtZNhJ7O33j4ETXoUOHEB8fb3FBSqsFKFu3bsXChQuRnJyMwYMH4+OPP8aIESOQn5/P+xOVVKQIesQufS7NtjQH/HqyFqYu8Zgy2yF0nATYuHFjixd1hmEEJYuGhobCxcUF6enp8PT0xJw5c6BQKKBWq7F27Vqkp6fDxcUFoaGhvMep++kUAJ5++mlER0dj27ZtSEtLa7JdUzQaDU6dOtVsm+zsbO78Lb59LlmyBEDDUlTjMbIXwF9++YV3n7o5J2K002g0WL16NQDgkUcewbRp09C9e3cUFBRg06ZNUKvVWL16NcLDwwXlZehu2Z49ezb3+163bp1Js3BOTk7Yv38/0tLSjPaZlpYGb29v3n1euHCB+xkZ648NSvlWxwYafpbLli0DALRr1w7R0dEYPXo0UlNTsW3bNtTX12PZsmWCfpbNzfRotVqkpaUhLS2N92vx3r17WLVqFdLS0uDm5obHH38cqampGD16NH777Tds3boVt2/fFhT0lJaWcsGJXC7HoEGDoFaroVAokJWVBY1Gg0OHDqG0tBReXpZTprvVApRVq1Zh7ty5eOmllwA0vFh+/vlnfP311/jXv/7VWsOyWWLPHBHpXLt2DQDg7OyM//znP2jX7u+XcX19Pd58803cvXsXffv25f3JUqPR4O7duwAaDm3s1q0bnJyc0K1bN/Tu3RtHjhzB3bt3BV382QuITCZDx44duTdqoOH5duPGDTAMo7dU05yTJ0+iuroabm5u+P7775GamsotS4wePRqTJ09GZWUlTp48iYEDB/LqMysrC/X1Dfldqamp3Jv9okWLsGDBAowaNQr19fXIysqCQqHg1Se7TOTn5weGYXDjxg3uPt0yAbrLSc35448/UFZWhtDQUCxdupRb1gsJCcHSpUvx+uuvIzc3F3/88Qe31NOS2tpabN++HV5eXti2bRv3HIqKisKIESMQHR2N7du3Y9asWbxnpMTuk51l8/T0NNrfpEmTUFZWpjcb15Ljx4+jpqYGcrkcP//8MzeOuXPnYsaMGRg9ejRqampw/PhxDB48mFef/v7+sLOzg4uLCz744AOD1+O//vUvVFVVwd/fn1d/ly9f5l4nlZWVXG4V+zcAwUEPG+ACDa91dtlRd/mRbff+++/zGuf90CoBSm1tLU6cOIFFixZxt9nZ2WHYsGEGPzAAqKmpQU3N32cANJVkI0W2uxRZ31Ilk7XUX0t96s54NO6zKW3hcUs5Rin6ZGccFAoFQkJCDO4fPHgwDh48KOiT5c6dO6HVajFu3DhkZWXpbX8NCAjA2LFjsWvXLuzcuRMTJ07k1efJkycBNLyBr1+/Hnl5eVxSa58+fTB9+nQUFxfj5MmTmDJlSov9sW/aL730Etq3b28wjhkzZuDzzz9HWloa7wCF3aX01FNPGfysnJycMHToUBw8eBDffvst7wCFDfI7dOiA1atXGzzu+fPnt1i8URc7a/TSSy8Z5BzZ2dlh5syZePPNN3Hq1CneAcrOnTuh0Wgwe/ZsvQsq0DCz8NJLL2HVqlWCft9i98meeD569Gij/Y0cORLff/89zp07hxEjRvAaY0pKCgDgueeeMwiSHBwcMH78ePz3v/9FSkoK7wBl37590Gq1eOWVV4y+HufOnYtVq1Zh3759vB73H3/8AQCYNm0aHn/8cYP7MzMzsXnzZkyaNIn3B5CzZxt2Gzz++OOYNm2awf0bNmzAoUOHuHZ83I8dUa0SoNy8eRMajcbg07afnx/3pNS1fPlyJCQktNivFNnuUmR9369kMnrc0vXZmo+7Q4cOOH/+PI4ePYr6+nqDT2zHjh3j2vF1/fp1AA0X+X/+858GO2RKS0uxa9curh0fbEJtUVERVCoVpkyZAoVCgYKCAqhUKq4YHN/E2+rqagBo8jgI9hMq244P9sNOUxejQYMG4eDBg4J2HrAzTGfOnDH6uNmLAN+ZKJmsIWm9pWU9th0f7O+xqaCLvV3I71vsPtnHe/78eWi1Wr3gTKvV4sKFC3rt+GCfa3369DF6f0hICP773/8KSgYX+3GzM27PPvssvL29De739vbG5s2bUVdXJ/jC7+npafQ9xpSyC/djR5RV7OJZtGgRFi5cyH1dUVGBLl0Mj6Xmu3WObcu3ndjb8cTukx43v7Zi9tmaj7tfv344fPgwqqqqMGnSJMyaNYtbm//666+5BNJ+/frxHmOnTp0ANEz5RkVFISwsTO9+dmaTbcfHgw8+iPPnz8Pb2xuXLl3Sm5Xx9/eHl5cXSktLeZ+I3bdvXxw6dAhr167FoEGDDC5YX3/9NdeOr+DgYBQXF2Pt2rUYNmxYk30a23rdlH79+mHjxo3o0qULLl++bDAb1aVLF1y5coX374ft79tvv0VYWJjBGNevX8+146vx77sxU37fYvfZuXNnAA3LMkqlElOmTOFyb7Zs2YITJ07oteOjd+/eOH/+PL766is8/vjjBj/LdevWce34EvtxS/G7GTx4MH7++Wf8/PPPiIuL05s9qq2txS+//MK140uK98rGZIyQ8FMktbW1aN++PbZv347x48dzt8+YMQNlZWX46aefmv3+iooKeHh4oLy8nEpFE5tTW1uLUaNGGezg0WVnZ4c9e/YIyh8YPXo03N3d9db7gYZZmejoaFRUVCA1NZV3n3fv3sXo0aMBNLzxDRkyBA4ODqitrcWRI0dw9OhRAA1r63zqMOg+7iFDhhgkix45ckTw475z5w5XJG7QoEGYPn061+eGDRuQldVQOXPXrl2865ZoNBpMnDgRZWVlGDJkiEEdlCNHjsDLywspKSm8ZlF0+1MoFJg6dSo3xs2bN0OtVgvqD5Dm9y12n2x/Tk5OcHV11cs18ff3R2VlJe7du2fWc/LFF1/kfpYbN24U/JyU8nFL9Vps164dJk6cyCUHb9++XS8PS0ghNVMIuX63ygyKg4MD+vfvj/T0dC5A0Wq1SE9Pb7XSz4RYCwcHB0yaNImrg9J4+6VWq8WkSZMEbbd1cHDAxIkTsXXrVkRHR+Oll17iZmW++eYblJaW4vnnnxfUp7OzM8LDw3Ho0CEcPXqUe/PXFR4ezvsNUfdxsxd6FvtJWOjjdnV1Re/evXHu3DlkZWVxAYmu3r178w5OgIalmwULFkClUuHUqVN643R0dIRMJsP8+fN5BxO6/Z08eVIvT8+U/gBpft9i96nbH/u7DwwMRGFhIX799VdUVVW1+nNS6sctxWuxvr4e33//Pb7//nuzHvf90CozKEDDNuMZM2bgiy++wKBBg/Dxxx9j27ZtOHfuXIs7QWgGhRDDSrJAw4XalEqyun02roshl8tNrr0ANF1/wdTiUMYet7ljjImJMZr/1rt3byQlJZnUZ+ODF4GGT/7mHLwoZn+ANL9vsfu0huekFOO0lsctlJDrd6sFKADw+eefc4XawsLC8Omnn/JaA6MAhZAGYlUBlbrPu3fv4ssvv8S1a9fQuXNnvPzyy2Z9WpNijHfu3MEHH3yAwsJCBAYG4l//+pegmRNjNBqNQcKxKWfISNUfYB3PIWt4TkoxTmt53EJYTYBiKgpQCCGEEOsj5PpteJAHIYQQQkgrowCFEEIIIRaHAhRCCCGEWBwKUAghhBBicShAIYQQQojFoQCFEEIIIRaHAhRCCCGEWBwKUAghhBBicShAIYQQQojFaZXDAs3FFr+tqKho5ZEQQgghhC/2us2niL1VBiiVlZUAgC5durTySAghhBAiVGVlJTw8PJptY5Vn8Wi1WhQWFsLNzQ0ymazJdhUVFejSpQuuXr0q2pk91tCnNYxRij6tYYxS9GkNY5SiT2sYoxR9WsMYpejTGsYoRZ/WMEYhfTIMg8rKSgQGBsLOrvksE6ucQbGzs0Pnzp15t3d3dxf9UEFr6NMaxihFn9YwRin6tIYxStGnNYxRij6tYYxS9GkNY5SiT2sYI98+W5o5YVGSLCGEEEIsDgUohBBCCLE4bTpAcXR0xOLFi+Ho6GhTfVrDGKXo0xrGKEWf1jBGKfq0hjFK0ac1jFGKPq1hjFL0aQ1jlKpPq0ySJYQQQkjb1qZnUAghhBBinShAIYQQQojFoQCFEEIIIRaHAhRCCCGEWJw2HaAkJiaiW7ducHJywuDBg5GVlWVyX5mZmRg7diwCAwMhk8mwY8cOs8a2fPlyDBw4EG5ubvD19cX48eORn59vVp9JSUno27cvVyhHoVBgz549ZvWp64MPPoBMJsP8+fNN7kOlUkEmk+n96d27t9lju379OqZNmwYfHx84OzsjNDQUx48fN7m/bt26GYxTJpMhNjbWpP40Gg2USiW6d+8OZ2dn9OjRA0uWLOF1HkVzKisrMX/+fHTt2hXOzs549NFHcezYMd7f39LzmmEYvPfeewgICICzszOGDRuGCxcumNzfDz/8gOHDh8PHxwcymQzZ2dlmjbGurg7vvPMOQkND4eLigsDAQEyfPh2FhYVmPW6VSoXevXvDxcUFXl5eGDZsGI4ePWpWn7rmzZsHmUyGjz/+2OT+Zs6cafD8HDlypNljPHv2LMaNGwcPDw+4uLhg4MCBuHLlisl9GnsdyWQyfPjhhyb1d+fOHcTFxaFz585wdnZGSEgIkpOTzXrcJSUlmDlzJgIDA9G+fXuMHDmy2ec5n/fve/fuITY2Fj4+PnB1dcWECRNQUlJiVp9ffvklhg4dCnd3d8hkMpSVlZnc3+3bt/Haa6+hV69ecHZ2RlBQEP75z3+ivLzcrDG+8sor6NGjB5ydndGxY0c888wzOHfuXJN9NqfNBihbt27FwoULsXjxYpw8eRL9+vXDiBEjcOPGDZP6q6qqQr9+/ZCYmCjK+DIyMhAbG4sjR44gLS0NdXV1GD58OKqqqkzus3Pnzvjggw9w4sQJHD9+HE899RSeeeYZ5OXlmT3eY8eO4YsvvkDfvn3N7qtPnz4oKiri/vz+++9m9VdaWorw8HDY29tjz549OHPmDD766CN4eXmZ3OexY8f0xpiWlgYAmDRpkkn9rVixAklJSfj8889x9uxZrFixAitXrsRnn31m8hgBYM6cOUhLS8PGjRuRk5OD4cOHY9iwYbh+/Tqv72/peb1y5Up8+umnSE5OxtGjR+Hi4oIRI0bg3r17JvVXVVWFxx57DCtWrOD3AFvos7q6GidPnoRSqcTJkyfxww8/ID8/H+PGjTO5TwD4xz/+gc8//xw5OTn4/fff0a1bNwwfPhx//fWXyX2yfvzxRxw5cgSBgYFmjREARo4cqfc8/e6778zq89KlS3jsscfQu3dvHDx4EKdPn4ZSqYSTk5PJfeqOr6ioCF9//TVkMhkmTJhgUn8LFy7EL7/8gk2bNuHs2bOYP38+4uLisHPnTpPGyDAMxo8fj8uXL+Onn37CqVOn0LVrVwwbNqzJ92M+798LFizArl27kJKSgoyMDBQWFuK5555rcox8+qyursbIkSPx7rvvNtkP3/4KCwtRWFiI//znP8jNzcW3336LX375BbNnzzZrjP3798c333yDs2fPYu/evWAYBsOHD4dGo2lxzAaYNmrQoEFMbGws97VGo2ECAwOZ5cuXm903AObHH380ux9dN27cYAAwGRkZovbr5eXFrF271qw+KisrmZ49ezJpaWnME088wbz++usm97V48WKmX79+Zo2nsXfeeYd57LHHRO2zsddff53p0aMHo9VqTfr+qKgoZtasWXq3Pffcc8zUqVNNHlN1dTUjl8uZ3bt3693+yCOPMP/3f/8nuL/Gz2utVsv4+/szH374IXdbWVkZ4+joyHz33XeC+9NVUFDAAGBOnTpl1hiNycrKYgAw//vf/0Trs7y8nAHA/Prrr2b1ee3aNaZTp05Mbm4u07VrV2b16tUm9zdjxgzmmWee4fX9fPt8/vnnmWnTponaZ2PPPPMM89RTT5ncX58+fZj3339f7zYhz/nGfebn5zMAmNzcXO42jUbDdOzYkfnqq6949dn4/busrIyxt7dnUlJSuDZnz55lADBqtdqkPnUdOHCAAcCUlpby6qul/ljbtm1jHBwcmLq6OtH6/OOPPxgAzMWLF3mPldUmZ1Bqa2tx4sQJDBs2jLvNzs4Ow4YNg1qtbsWRNY2dVvP29halP41Gg++//x5VVVVQKBRm9RUbG4uoqCi9n6c5Lly4gMDAQAQHB2Pq1KnNTh/zsXPnTgwYMACTJk2Cr68vHn74YXz11VeijBVoeD5t2rQJs2bNavZwyuY8+uijSE9Px/nz5wEAf/zxB37//XeMGjXK5HHV19dDo9EYfLp1dnY2e1YKAAoKClBcXKz3e/fw8MDgwYMt9nUENLyWZDIZPD09RemvtrYWX375JTw8PNCvXz+T+9FqtXjxxRfx1ltvoU+fPqKM7eDBg/D19UWvXr0QExODW7dumTW+n3/+Gf/4xz8wYsQI+Pr6YvDgwWYvZ+sqKSnBzz//3Oyn9JY8+uij2LlzJ65fvw6GYXDgwAGcP38ew4cPN6m/mpoaANB7HdnZ2cHR0ZH366jx+/eJEydQV1en99rp3bs3goKCeL92xL4m8OmvvLwc7u7uaNeO3zF9LfVZVVWFb775Bt27d0eXLl0EjriNLvHcvHkTGo0Gfn5+erf7+fmhuLi4lUbVNK1Wi/nz5yM8PBwPPfSQWX3l5OTA1dUVjo6OmDdvHn788UeEhISY3N/333+PkydPYvny5WaNizV48GBuKjEpKQkFBQV4/PHHUVlZaXKfly9fRlJSEnr27Im9e/ciJiYG//znP7F+/XpRxrxjxw6UlZVh5syZJvfxr3/9C5MnT0bv3r1hb2+Phx9+GPPnz8fUqVNN7tPNzQ0KhQJLlixBYWEhNBoNNm3aBLVajaKiIpP7ZbGvFWt5HQEN6/7vvPMOXnjhBbMPQdu9ezdcXV3h5OSE1atXIy0tDR06dDC5vxUrVqBdu3b45z//ada4WCNHjsSGDRuQnp6OFStWICMjA6NGjTJtKh3AjRs3cOfOHXzwwQcYOXIk9u3bh2effRbPPfccMjIyRBnz+vXr4ebm1uxSR0s+++wzhISEoHPnznBwcMDIkSORmJiIiIgIk/pjA4dFixahtLQUtbW1WLFiBa5du8brdWTs/bu4uBgODg4GQTLf146Y1wS+/d28eRNLlizByy+/bHafa9asgaurK1xdXbFnzx6kpaXBwcFB8Lit8jTjtiY2Nha5ubmifOrt1asXsrOzUV5eju3bt2PGjBnIyMgwKUi5evUqXn/9daSlpTW7Bi2E7oxB3759MXjwYHTt2hXbtm0z+VOVVqvFgAEDsGzZMgDAww8/jNzcXCQnJ2PGjBlmj3ndunUYNWpUizkDzdm2bRs2b96MLVu2oE+fPsjOzsb8+fMRGBho1hg3btyIWbNmoVOnTpDL5XjkkUfwwgsv4MSJEyb3aa3q6uoQHR0NhmGQlJRkdn9PPvkksrOzcfPmTXz11VeIjo7G0aNH4evrK7ivEydO4JNPPsHJkydNnoVrbPLkydy/Q0ND0bdvX/To0QMHDx5EZGSk4P60Wi0A4JlnnsGCBQsAAGFhYTh8+DCSk5PxxBNPmD3mr7/+GlOnTjXr/eSzzz7DkSNHsHPnTnTt2hWZmZmIjY1FYGCgSbO89vb2+OGHHzB79mx4e3tDLpdj2LBhGDVqFK8kdjHfv6Xqs6X+KioqEBUVhZCQEKhUKrP7nDp1Kp5++mkUFRXhP//5D6Kjo3Ho0CHhv3fBi0JWoKamhpHL5QZrl9OnT2fGjRtndv8QMQclNjaW6dy5M3P58mVR+mssMjKSefnll0363h9//JEBwMjlcu4PAEYmkzFyuZypr68XZYwDBgxg/vWvf5n8/UFBQczs2bP1bluzZg0TGBho7tCYP//8k7Gzs2N27NhhVj+dO3dmPv/8c73blixZwvTq1cusfll37txhCgsLGYZhmOjoaGb06NGC+2j8vL506ZLRPJGIiAjmn//8p+D+dImdg1JbW8uMHz+e6du3L3Pz5k1R+mzsgQceYJYtW2ZSn6tXr+ZeN7qvJTs7O6Zr166ijbFDhw5McnKySWOsqalh2rVrxyxZskSv3dtvv808+uijJvWpKzMzkwHAZGdn8+rLWH/V1dWMvb29Qd7V7NmzmREjRpg9xrKyMubGjRsMwzTkMb766qvN9tXU+3d6errRHJGgoCBm1apVJvWpS0gOSkv9VVRUMAqFgomMjGTu3r3bYn98x8iqqalh2rdvz2zZsoVX37ra5BKPg4MD+vfvj/T0dO42rVaL9PR0s/MxxMIwDOLi4vDjjz9i//796N69uyT/j1ar5dZYhYqMjEROTg6ys7O5PwMGDMDUqVORnZ0NuVxu9vju3LmDS5cuISAgwOQ+wsPDDba6nT9/Hl27djV3ePjmm2/g6+uLqKgos/qprq6GnZ3+y00ul3OfWs3l4uKCgIAAlJaWYu/evXjmmWfM7rN79+7w9/fXex1VVFTg6NGjFvM6Av6eOblw4QJ+/fVX+Pj4SPL/mPNaevHFF3H69Gm911JgYCDeeust7N27V5TxXbt2Dbdu3TL5teTg4ICBAwdK9lpat24d+vfvb1YeT11dHerq6iR7LXl4eKBjx464cOECjh8/3uTrqKX37/79+8Pe3l7vtZOfn48rV640+doR+5rAp7+KigoMHz4cDg4O2LlzZ4szHKaMkWEYMAxj0munzS7xLFy4EDNmzMCAAQMwaNAgfPzxx6iqqsJLL71kUn937tzBxYsXua8LCgqQnZ0Nb29vBAUFCe4vNjYWW7ZswU8//QQ3NzduXdLDwwPOzs4mjXHRokUYNWoUgoKCUFlZiS1btuDgwYMmvwG6ubkZrC26uLjAx8fH5HXRN998E2PHjkXXrl1RWFiIxYsXQy6X44UXXjCpP6BhO9+jjz6KZcuWITo6GllZWfjyyy/x5Zdfmtwn0HBB+uabbzBjxgzeSWNNGTt2LP79738jKCgIffr0walTp7Bq1SrMmjXLrH7ZbXy9evXCxYsX8dZbb6F37968n+ctPa/nz5+PpUuXomfPnujevTuUSiUCAwMxfvx4k/q7ffs2rly5wtUpYS+G/v7+8Pf3F9xnQEAAJk6ciJMnT2L37t3QaDTca8nb27vJde/m+vTx8cG///1vjBs3DgEBAbh58yYSExNx/fr1ZreZt/TYGwdO9vb28Pf3R69evQT35+3tjYSEBEyYMAH+/v64dOkS3n77bTzwwAMYMWKEyWN866238PzzzyMiIgJPPvkkfvnlF+zatQsHDx40uU+g4UKYkpKCjz76qMl++Pb3xBNP4K233oKzszO6du2KjIwMbNiwAatWrTK5z5SUFHTs2BFBQUHIycnB66+/jvHjxzeZeNvS+7eHhwdmz56NhQsXwtvbG+7u7njttdegUCgwZMgQk/oEGnJbiouLuceSk5MDNzc3BAUFGSSqttQfG5xUV1dj06ZNqKioQEVFBQCgY8eORj+AttTn5cuXsXXrVgwfPhwdO3bEtWvX8MEHH8DZ2RmjR49u8vfTJMFzLlbks88+Y4KCghgHBwdm0KBBzJEjR0zui51Sa/xnxowZJvVnrC8AzDfffGPyGGfNmsV07dqVcXBwYDp27MhERkYy+/btM7k/Y8zdZvz8888zAQEBjIODA9OpUyfm+eefN2n7WWO7du1iHnroIcbR0ZHp3bs38+WXX5rd5969exkATH5+vtl9VVRUMK+//joTFBTEODk5McHBwcz//d//MTU1NWb1u3XrViY4OJhxcHBg/P39mdjYWKasrIz397f0vNZqtYxSqWT8/PwYR0dHJjIystmfR0v9ffPNN0bvX7x4sUl9sktFxv4cOHDApD7v3r3LPPvss0xgYCDj4ODABAQEMOPGjWOysrLM+lk21tI24+b6q66uZoYPH8507NiRsbe3Z7p27crMnTuXKS4uNnuM69atYx544AHGycmJ6devX4vLm3z6/OKLLxhnZ2dez82W+isqKmJmzpzJBAYGMk5OTkyvXr2Yjz76qNkSAC31+cknnzCdO3dm7O3tmaCgICY+Pr7Z1yaf9++7d+8yr776KuPl5cW0b9+eefbZZ5mioiKz+ly8eDHv60ZL/TX1MwHAFBQUmDTG69evM6NGjWJ8fX0Ze3t7pnPnzsyUKVOYc+fONfm4myP7//8pIYQQQojFaJM5KIQQQgixbhSgEEIIIcTiUIBCCCGEEItDAQohhBBCLA4FKIQQQgixOBSgEEIIIcTiUIBCCCGEEItDAQohhBBCLA4FKIQQSQwdOhTz589v7WEQQqwUBSiEEEIIsTgUoBBC2oza2trWHgIhRCQUoBBCJKPVavH222/D29sb/v7+UKlU3H1XrlzBM888A1dXV7i7uyM6OholJSXc/TNnzjQ4NXn+/PkYOnQo9/XQoUMRFxeH+fPno0OHDs2e5EsIsS4UoBBCJLN+/Xq4uLjg6NGjWLlyJd5//32kpaVBq9XimWeewe3bt5GRkYG0tDRcvnwZzz//vEn/h4ODAw4dOoTk5GQJHgUhpDW0a+0BEELarr59+2Lx4sUAgJ49e+Lzzz9Heno6ACAnJwcFBQXo0qULAGDDhg3o06cPjh07hoEDB/L+P3r27ImVK1eKP3hCSKuiGRRCiGT69u2r93VAQABu3LiBs2fPokuXLlxwAgAhISHw9PTE2bNnBf0f/fv3F2WshBDLQgEKIUQy9vb2el/LZDJotVpe32tnZweGYfRuq6urM2jn4uJi+gAJIRaLAhRCyH334IMP4urVq7h69Sp325kzZ1BWVoaQkBAAQMeOHVFUVKT3fdnZ2fdzmISQVkQBCiHkvhs2bBhCQ0MxdepUnDx5EllZWZg+fTqeeOIJDBgwAADw1FNP4fjx49iwYQMuXLiAxYsXIzc3t5VHTgi5XyhAIYTcdzKZDD/99BO8vLwQERGBYcOGITg4GFu3buXajBgxAkqlEm+//TYGDhyIyspKTJ8+vRVHTQi5n2RM40VeQgghhJBWRjMohBBCCLE4FKAQQgghxOJQgEIIIYQQi0MBCiGEEEIsDgUohBBCCLE4FKAQQgghxOJQgEIIIYQQi0MBCiGEEEIsDgUohBBCCLE4FKAQQgghxOJQgEIIIYQQi/P/AKUKdLYGu51nAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data=report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc79e44f-91ec-4ce4-bac5-c9f47e113be1",
   "metadata": {},
   "source": [
    "**OBSERVATION**: Looks like numer of calls in day time is more.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8c2f00dc-f05e-4c4d-8a40-ad99b727796c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hour\n",
       "0      7.0\n",
       "1      6.0\n",
       "2      6.0\n",
       "3      5.0\n",
       "4      5.0\n",
       "5      6.0\n",
       "6      9.0\n",
       "7     15.0\n",
       "8     19.0\n",
       "9     20.0\n",
       "10    21.0\n",
       "11    22.0\n",
       "12    23.0\n",
       "13    23.0\n",
       "14    23.0\n",
       "15    24.0\n",
       "16    24.0\n",
       "17    25.0\n",
       "18    21.0\n",
       "19    18.0\n",
       "20    16.0\n",
       "21    14.0\n",
       "22    11.0\n",
       "23     9.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e4e999f9-d256-4149-9b35-0310a363d635",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='hour'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(report, estimator=np.median)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "815f810e-6f7e-454a-9701-b226ab4f6a56",
   "metadata": {},
   "source": [
    "**Observations**\n",
    "1. Non negative\n",
    "2. Data is Discrete - Number of calls cannot be 2.5\n",
    "\n",
    "**Assumptions:**\n",
    "1. Each call(i.e. an event) is independent of other.\n",
    "2. Assuming that the call every hour are coming at a constant rate. (Lambda) - i.e. During 5PM to 6PM we are getting 20, 23, 26, 20, 25, 30, etc... calls across multiple days. Lambda is the median number of calls across all the days during 5PM to 6PM.\n",
    "3. You have historical data that shows, on average, you receive about 25 emergency calls per hour during peak times (i.e. lambda = 25).\n",
    "\n",
    "**Objective:**\n",
    "You want to use the Poisson distribution to estimate the probabilities of different numbers of emergency calls arriving in the next hour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6a217c1d-8e57-416a-8372-f719dc635603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>hour</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-12-10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-11</th>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-12</th>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-13</th>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>...</td>\n",
       "      <td>18.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-14</th>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "hour         0     1    2     3    4     5     6     7     8     9   ...  \\\n",
       "date                                                                 ...   \n",
       "2015-12-10  0.0   0.0  0.0   0.0  0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "2015-12-11  3.0   4.0  4.0   9.0  6.0   5.0  10.0  11.0  29.0  11.0  ...   \n",
       "2015-12-12  7.0   5.0  5.0  11.0  5.0  11.0   3.0  14.0   5.0  12.0  ...   \n",
       "2015-12-13  9.0   5.0  5.0   8.0  2.0   4.0   8.0  13.0  14.0  11.0  ...   \n",
       "2015-12-14  4.0  11.0  4.0   3.0  4.0  10.0  15.0  27.0  25.0  21.0  ...   \n",
       "\n",
       "hour          14    15    16    17    18    19    20    21    22    23  \n",
       "date                                                                    \n",
       "2015-12-10   1.0   1.0   6.0  16.0  26.0  20.0  15.0  11.0  10.0   8.0  \n",
       "2015-12-11  23.0  28.0  27.0  39.0  24.0  32.0  11.0  12.0  16.0   7.0  \n",
       "2015-12-12  20.0  28.0  30.0  22.0  24.0  37.0   9.0  29.0  21.0  19.0  \n",
       "2015-12-13  18.0  17.0  14.0  22.0  28.0  16.0  14.0   8.0  17.0   6.0  \n",
       "2015-12-14  16.0  28.0  33.0  31.0  38.0  22.0  20.0  19.0  10.0  10.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8afe5e62-8cec-448e-90f8-5fc1d0d21a09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23],\n",
       "      dtype='int32', name='hour')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cd13f5e4-0b91-4db3-b9a0-2913bde0252a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the Hour: 17\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input Accepted\n"
     ]
    }
   ],
   "source": [
    "h = int(input(\"Enter the Hour:\"))\n",
    "\n",
    "while h < 0 or h > 23:\n",
    "    print(\"Incorrect Input. Note that the input should be an integer in the range 0 to 23.\")\n",
    "    h = int(input(\"Enter the Hour:\"))\n",
    "else:\n",
    "    print(\"Input Accepted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ab8a7db5-e1b5-436f-9cd2-8f88fbc481ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2015-12-10    16.0\n",
       "2015-12-11    39.0\n",
       "2015-12-12    22.0\n",
       "2015-12-13    22.0\n",
       "2015-12-14    31.0\n",
       "              ... \n",
       "2020-07-25    18.0\n",
       "2020-07-26    19.0\n",
       "2020-07-27    15.0\n",
       "2020-07-28    16.0\n",
       "2020-07-29     0.0\n",
       "Name: 17, Length: 1694, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report[h]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f275fb1f-f99a-40e4-a424-011a3a1d3dbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17\n",
       "25.0     86\n",
       "26.0     85\n",
       "22.0     84\n",
       "30.0     82\n",
       "21.0     82\n",
       "         ..\n",
       "59.0      1\n",
       "57.0      1\n",
       "118.0     1\n",
       "64.0      1\n",
       "51.0      1\n",
       "Name: count, Length: 68, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "report[h].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "98049533-d07b-4675-8f0b-2c2022cf2f45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='17'>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report[h].value_counts().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f4001e52-dfbc-4292-b730-16f0f3831371",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='17'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "report[h].value_counts().sort_index().plot(kind=\"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a78b855f-8776-4147-ab8b-510f12880688",
   "metadata": {},
   "source": [
    "**OBSERVATION**: Looks like a Poisson Distribution (Discrete Distribution, Right Skewed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1ae4aac3-6a9b-4d1c-a8a8-9c4b9cd868e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_calls</th>\n",
       "      <th>prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>0.0099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>0.0155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>0.0227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>0.0316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>0.0415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20</td>\n",
       "      <td>0.0519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21</td>\n",
       "      <td>0.0618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>22</td>\n",
       "      <td>0.0702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>23</td>\n",
       "      <td>0.0763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>24</td>\n",
       "      <td>0.0795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>25</td>\n",
       "      <td>0.0795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>26</td>\n",
       "      <td>0.0765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>27</td>\n",
       "      <td>0.0708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>28</td>\n",
       "      <td>0.0632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>29</td>\n",
       "      <td>0.0545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>30</td>\n",
       "      <td>0.0454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>31</td>\n",
       "      <td>0.0366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>32</td>\n",
       "      <td>0.0286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>33</td>\n",
       "      <td>0.0217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>34</td>\n",
       "      <td>0.0159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    n_calls    prob\n",
       "0        15  0.0099\n",
       "1        16  0.0155\n",
       "2        17  0.0227\n",
       "3        18  0.0316\n",
       "4        19  0.0415\n",
       "5        20  0.0519\n",
       "6        21  0.0618\n",
       "7        22  0.0702\n",
       "8        23  0.0763\n",
       "9        24  0.0795\n",
       "10       25  0.0795\n",
       "11       26  0.0765\n",
       "12       27  0.0708\n",
       "13       28  0.0632\n",
       "14       29  0.0545\n",
       "15       30  0.0454\n",
       "16       31  0.0366\n",
       "17       32  0.0286\n",
       "18       33  0.0217\n",
       "19       34  0.0159"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define the average arrival rate (lambda) for the Poisson distribution\n",
    "lambda_ = report[h].median()\n",
    "\n",
    "# Define the possible number of emergency calls\n",
    "min_th = int(lambda_) - 10\n",
    "max_th = int(lambda_) + 10\n",
    "\n",
    "num_calls = range(min_th, max_th)\n",
    "\n",
    "# Calculate the probabilities for different numbers of calls\n",
    "# And print the probabilities\n",
    "est = []\n",
    "for num in num_calls:\n",
    "    if num >= 0:\n",
    "        est.append([num, round(stats.poisson.pmf(num, mu=lambda_), 4)])\n",
    "\n",
    "est_df = pd.DataFrame(est, columns=[\"n_calls\", \"prob\"])\n",
    "\n",
    "est_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f0ca3e3-9fa7-43de-b433-438d52cbfb71",
   "metadata": {},
   "source": [
    "**Explanation:**\n",
    "We use the Poisson distribution to estimate the probabilities of different numbers of emergency calls arriving in the next hour. The `mu` parameter is set to the average arrival rate.\n",
    "\n",
    "The loop then prints out the probability of each possible number of calls.\n",
    "\n",
    "**Interpretation:**\n",
    "This code will give you the probabilities of different numbers of emergency calls arriving in the next hour. For instance, it might tell you that there's a 10% chance of receiving exactly 25 emergency calls and a 5% chance of receiving exactly 35 calls.\n",
    "\n",
    "By using this information, you can make decisions about how many operators to schedule, how many phone lines to keep open, and how to allocate resources effectively during different times of the day."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc266640-3627-40b9-a67c-e38b63aaf1ef",
   "metadata": {},
   "source": [
    "### Preparing the Script for Data App"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f4b49e3a-96be-413f-b108-42f2769e302f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "      <th>desc</th>\n",
       "      <th>zip</th>\n",
       "      <th>title</th>\n",
       "      <th>timeStamp</th>\n",
       "      <th>twp</th>\n",
       "      <th>addr</th>\n",
       "      <th>e</th>\n",
       "      <th>hour</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.297876</td>\n",
       "      <td>-75.581294</td>\n",
       "      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n",
       "      <td>19525.0</td>\n",
       "      <td>EMS: BACK PAINS/INJURY</td>\n",
       "      <td>2015-12-10 17:10:52</td>\n",
       "      <td>NEW HANOVER</td>\n",
       "      <td>REINDEER CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40.258061</td>\n",
       "      <td>-75.264680</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n",
       "      <td>19446.0</td>\n",
       "      <td>EMS: DIABETIC EMERGENCY</td>\n",
       "      <td>2015-12-10 17:29:21</td>\n",
       "      <td>HATFIELD TOWNSHIP</td>\n",
       "      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.121182</td>\n",
       "      <td>-75.351975</td>\n",
       "      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>Fire: GAS-ODOR/LEAK</td>\n",
       "      <td>2015-12-10 14:39:21</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>HAWS AVE</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40.116153</td>\n",
       "      <td>-75.343513</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n",
       "      <td>19401.0</td>\n",
       "      <td>EMS: CARDIAC EMERGENCY</td>\n",
       "      <td>2015-12-10 16:47:36</td>\n",
       "      <td>NORRISTOWN</td>\n",
       "      <td>AIRY ST &amp; SWEDE ST</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.251492</td>\n",
       "      <td>-75.603350</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMS: DIZZINESS</td>\n",
       "      <td>2015-12-10 16:56:52</td>\n",
       "      <td>LOWER POTTSGROVE</td>\n",
       "      <td>CHERRYWOOD CT &amp; DEAD END</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>2015-12-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         lat        lng                                               desc  \\\n",
       "0  40.297876 -75.581294  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n",
       "1  40.258061 -75.264680  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n",
       "2  40.121182 -75.351975  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n",
       "3  40.116153 -75.343513  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n",
       "4  40.251492 -75.603350  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n",
       "\n",
       "       zip                    title           timeStamp                twp  \\\n",
       "0  19525.0   EMS: BACK PAINS/INJURY 2015-12-10 17:10:52        NEW HANOVER   \n",
       "1  19446.0  EMS: DIABETIC EMERGENCY 2015-12-10 17:29:21  HATFIELD TOWNSHIP   \n",
       "2  19401.0      Fire: GAS-ODOR/LEAK 2015-12-10 14:39:21         NORRISTOWN   \n",
       "3  19401.0   EMS: CARDIAC EMERGENCY 2015-12-10 16:47:36         NORRISTOWN   \n",
       "4      NaN           EMS: DIZZINESS 2015-12-10 16:56:52   LOWER POTTSGROVE   \n",
       "\n",
       "                         addr  e  hour        date  \n",
       "0      REINDEER CT & DEAD END  1    17  2015-12-10  \n",
       "1  BRIAR PATH & WHITEMARSH LN  1    17  2015-12-10  \n",
       "2                    HAWS AVE  1    14  2015-12-10  \n",
       "3          AIRY ST & SWEDE ST  1    16  2015-12-10  \n",
       "4    CHERRYWOOD CT & DEAD END  1    16  2015-12-10  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2a37c7c5-1f1d-4001-a139-ba8579e94457",
   "metadata": {},
   "outputs": [],
   "source": [
    "map_data = df[['lat', 'lng']]\n",
    "\n",
    "map_data.columns = ['lat', 'lon']\n",
    "\n",
    "map_data.to_csv(\"data/call_center/map_data.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e821fda8-9e6d-4bcf-b245-3e29fc8614d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "report.to_csv(\"data/call_center/day_wise_hourly_report.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f8c4c9d3-97f0-433c-8c33-b68297cec38a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotter(report, hour):\n",
    "    fig, ax = plt.subplots(figsize=(20, 8))\n",
    "\n",
    "    ax.set_title(\"Distribution of Calls\")\n",
    "    sns.countplot(data=report, x=hour, ax=ax)\n",
    "    ax.set_xlabel(f\"Number of Calls at {hour} hour\")\n",
    "    ax.set_ylabel(\"Frequency\")\n",
    "    \n",
    "    plt.xticks(rotation=45)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "def estimation(report, hour):\n",
    "    # Define the average arrival rate (lambda) for the Poisson distribution\n",
    "    lambda_ = report.median()[hour]\n",
    "    \n",
    "    # Define the possible number of emergency calls\n",
    "    min_th = int(lambda_) - 10\n",
    "    max_th = int(lambda_) + 10\n",
    "\n",
    "    num_calls = range(min_th, max_th)\n",
    "\n",
    "    est = []\n",
    "    # Calculate the probabilities for different numbers of calls\n",
    "    # And print the probabilities\n",
    "    for num in num_calls:\n",
    "        if num >= 0:\n",
    "            est.append([num, round(stats.poisson.pmf(num, mu=lambda_), 4)])\n",
    "    est_df = pd.DataFrame(est, columns=[\"n_calls\", \"prob\"])\n",
    "    \n",
    "    return est_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6962ed80-bbaa-4622-b25c-fe2c3877a46c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Enter the Hour: 17\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input Accepted\n"
     ]
    }
   ],
   "source": [
    "hour = int(input(\"Enter the Hour:\"))\n",
    "\n",
    "while hour < 0 or hour > 23:\n",
    "    print(\"Incorrect Input. Note that the input should be an integer in the range 0 to 23.\")\n",
    "    hour = int(input(\"Enter the Hour:\"))\n",
    "else:\n",
    "    print(\"Input Accepted\")\n",
    "\n",
    "report_df = pd.read_csv(\"data/call_center/day_wise_hourly_report.csv\")\n",
    "\n",
    "report_df.set_index('date', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4fa64ade-5891-4688-a558-5938ed856208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(report_df, estimator=np.median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "947b0f31-1b20-4749-a57d-28f30b18bc6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotter(report_df, str(hour))"
   ]
  },
  {
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   "execution_count": 43,
   "id": "91c40398-6ca0-4804-a216-367f5353560e",
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   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>n_calls</th>\n",
       "      <th>prob</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.0765</td>\n",
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       "      <th>12</th>\n",
       "      <td>27</td>\n",
       "      <td>0.0708</td>\n",
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       "      <th>13</th>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>34</td>\n",
       "      <td>0.0159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    n_calls    prob\n",
       "0        15  0.0099\n",
       "1        16  0.0155\n",
       "2        17  0.0227\n",
       "3        18  0.0316\n",
       "4        19  0.0415\n",
       "5        20  0.0519\n",
       "6        21  0.0618\n",
       "7        22  0.0702\n",
       "8        23  0.0763\n",
       "9        24  0.0795\n",
       "10       25  0.0795\n",
       "11       26  0.0765\n",
       "12       27  0.0708\n",
       "13       28  0.0632\n",
       "14       29  0.0545\n",
       "15       30  0.0454\n",
       "16       31  0.0366\n",
       "17       32  0.0286\n",
       "18       33  0.0217\n",
       "19       34  0.0159"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimation(report_df, str(hour))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "25456eab-354c-4dc4-b04d-9697b5b3c548",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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